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https://paperswithcode.com/paper/interior-point-methods-with-adversarial
|
1805.09293
| null | null |
Learning to Optimize Contextually Constrained Problems for Real-Time Decision-Generation
|
The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this work, we combine techniques from both fields to address the problem of learning to generate decisions to instances of continuous optimization problems where the feasible set varies with contextual features. We propose a novel framework for training a generative model to estimate optimal decisions by combining interior point methods and adversarial learning, which we further embed within an data generation algorithm. Decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Finally, we investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively.
| null |
https://arxiv.org/abs/1805.09293v4
|
https://arxiv.org/pdf/1805.09293v4.pdf
| null |
[
"Aaron Babier",
"Timothy C. Y. Chan",
"Adam Diamant",
"Rafid Mahmood"
] |
[
"Active Learning",
"Portfolio Optimization",
"Stochastic Optimization"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-universal-music-translation-network
|
1805.07848
| null | null |
A Universal Music Translation Network
|
We present a method for translating music across musical instruments, genres,
and styles. This method is based on a multi-domain wavenet autoencoder, with a
shared encoder and a disentangled latent space that is trained end-to-end on
waveforms. Employing a diverse training dataset and large net capacity, the
domain-independent encoder allows us to translate even from musical domains
that were not seen during training. The method is unsupervised and does not
rely on supervision in the form of matched samples between domains or musical
transcriptions. We evaluate our method on NSynth, as well as on a dataset
collected from professional musicians, and achieve convincing translations,
even when translating from whistling, potentially enabling the creation of
instrumental music by untrained humans.
|
We present a method for translating music across musical instruments, genres, and styles.
|
http://arxiv.org/abs/1805.07848v2
|
http://arxiv.org/pdf/1805.07848v2.pdf
| null |
[
"Noam Mor",
"Lior Wolf",
"Adam Polyak",
"Yaniv Taigman"
] |
[
"Translation"
] | 2018-05-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Mixture of Logistic Distributions (MoL)** is a type of output function, and an alternative to a [softmax](https://paperswithcode.com/method/softmax) layer. Discretized logistic mixture likelihood is used in [PixelCNN](https://paperswithcode.com/method/pixelcnn)++ and [WaveNet](https://paperswithcode.com/method/wavenet) to predict discrete values.\r\n\r\nImage Credit: [Hao Gao](https://medium.com/@smallfishbigsea/an-explanation-of-discretized-logistic-mixture-likelihood-bdfe531751f0)",
"full_name": "Mixture of Logistic Distributions",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Mixture of Logistic Distributions",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "A **Dilated Causal Convolution** is a [causal convolution](https://paperswithcode.com/method/causal-convolution) where the filter is applied over an area larger than its length by skipping input values with a certain step. A dilated causal [convolution](https://paperswithcode.com/method/convolution) effectively allows the network to have very large receptive fields with just a few layers.",
"full_name": "Dilated Causal Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Temporal Convolutions",
"parent": null
},
"name": "Dilated Causal Convolution",
"source_title": "WaveNet: A Generative Model for Raw Audio",
"source_url": "http://arxiv.org/abs/1609.03499v2"
},
{
"code_snippet_url": null,
"description": "**WaveNet** is an audio generative model based on the [PixelCNN](https://paperswithcode.com/method/pixelcnn) architecture. In order to deal with long-range temporal dependencies needed for raw audio generation, architectures are developed based on dilated causal convolutions, which exhibit very large receptive fields.\r\n\r\nThe joint probability of a waveform $\\vec{x} = \\{ x_1, \\dots, x_T \\}$ is factorised as a product of conditional probabilities as follows:\r\n\r\n$$p\\left(\\vec{x}\\right) = \\prod_{t=1}^{T} p\\left(x_t \\mid x_1, \\dots ,x_{t-1}\\right)$$\r\n\r\nEach audio sample $x_t$ is therefore conditioned on the samples at all previous timesteps.",
"full_name": "WaveNet",
"introduced_year": 2000,
"main_collection": {
"area": "Audio",
"description": "",
"name": "Generative Audio Models",
"parent": null
},
"name": "WaveNet",
"source_title": "WaveNet: A Generative Model for Raw Audio",
"source_url": "http://arxiv.org/abs/1609.03499v2"
}
] |
https://paperswithcode.com/paper/on-nesting-monte-carlo-estimators
|
1709.06181
| null | null |
On Nesting Monte Carlo Estimators
|
Many problems in machine learning and statistics involve nested expectations
and thus do not permit conventional Monte Carlo (MC) estimation. For such
problems, one must nest estimators, such that terms in an outer estimator
themselves involve calculation of a separate, nested, estimation. We
investigate the statistical implications of nesting MC estimators, including
cases of multiple levels of nesting, and establish the conditions under which
they converge. We derive corresponding rates of convergence and provide
empirical evidence that these rates are observed in practice. We further
establish a number of pitfalls that can arise from naive nesting of MC
estimators, provide guidelines about how these can be avoided, and lay out
novel methods for reformulating certain classes of nested expectation problems
into single expectations, leading to improved convergence rates. We demonstrate
the applicability of our work by using our results to develop a new estimator
for discrete Bayesian experimental design problems and derive error bounds for
a class of variational objectives.
| null |
http://arxiv.org/abs/1709.06181v4
|
http://arxiv.org/pdf/1709.06181v4.pdf
|
ICML 2018 7
|
[
"Tom Rainforth",
"Robert Cornish",
"Hongseok Yang",
"Andrew Warrington",
"Frank Wood"
] |
[
"Experimental Design"
] | 2017-09-18T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1874
|
http://proceedings.mlr.press/v80/rainforth18a/rainforth18a.pdf
|
on-nesting-monte-carlo-estimators-1
| null |
[] |
https://paperswithcode.com/paper/machine-learning-inference-of-fluid-variables
|
1805.09917
| null | null |
Machine-learning inference of fluid variables from data using reservoir computing
|
We infer both microscopic and macroscopic behaviors of a three-dimensional
chaotic fluid flow using reservoir computing. In our procedure of the
inference, we assume no prior knowledge of a physical process of a fluid flow
except that its behavior is complex but deterministic. We present two ways of
inference of the complex behavior; the first called partial-inference requires
continued knowledge of partial time-series data during the inference as well as
past time-series data, while the second called full-inference requires only
past time-series data as training data. For the first case, we are able to
infer long-time motion of microscopic fluid variables. For the second case, we
show that the reservoir dynamics constructed from only past data of energy
functions can infer the future behavior of energy functions and reproduce the
energy spectrum. It is also shown that we can infer a time-series data from
only one measurement by using the delay coordinates. These implies that the
obtained two reservoir systems constructed without the knowledge of microscopic
data are equivalent to the dynamical systems describing macroscopic behavior of
energy functions.
| null |
http://arxiv.org/abs/1805.09917v3
|
http://arxiv.org/pdf/1805.09917v3.pdf
| null |
[
"Kengo Nakai",
"Yoshitaka Saiki"
] |
[
"BIG-bench Machine Learning",
"Time Series",
"Time Series Analysis"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-self-play-computation-of-equilibrium-in
|
1805.09282
| null | null |
On self-play computation of equilibrium in poker
|
We compare performance of the genetic algorithm and the counterfactual regret
minimization algorithm in computing the near-equilibrium strategies in the
simplified poker games. We focus on the von Neumann poker and the simplified
version of the Texas Hold'Em poker, and test outputs of the considered
algorithms against analytical expressions defining the Nash equilibrium
strategies. We comment on the performance of the studied algorithms against
opponents deviating from equilibrium.
| null |
http://arxiv.org/abs/1805.09282v1
|
http://arxiv.org/pdf/1805.09282v1.pdf
| null |
[
"Mikhail Goykhman"
] |
[
"counterfactual"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/variational-inference-for-data-efficient
|
1805.09281
| null | null |
Variational Inference for Data-Efficient Model Learning in POMDPs
|
Partially observable Markov decision processes (POMDPs) are a powerful
abstraction for tasks that require decision making under uncertainty, and
capture a wide range of real world tasks. Today, effective planning approaches
exist that generate effective strategies given black-box models of a POMDP
task. Yet, an open question is how to acquire accurate models for complex
domains. In this paper we propose DELIP, an approach to model learning for
POMDPs that utilizes amortized structured variational inference. We empirically
show that our model leads to effective control strategies when coupled with
state-of-the-art planners. Intuitively, model-based approaches should be
particularly beneficial in environments with changing reward structures, or
where rewards are initially unknown. Our experiments confirm that DELIP is
particularly effective in this setting.
| null |
http://arxiv.org/abs/1805.09281v1
|
http://arxiv.org/pdf/1805.09281v1.pdf
| null |
[
"Sebastian Tschiatschek",
"Kai Arulkumaran",
"Jan Stühmer",
"Katja Hofmann"
] |
[
"Decision Making",
"Decision Making Under Uncertainty",
"Open-Ended Question Answering",
"Variational Inference"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/wisenetmd-motion-detection-using-dynamic
|
1805.09277
| null | null |
WisenetMD: Motion Detection Using Dynamic Background Region Analysis
|
Motion detection algorithms that can be applied to surveillance cameras such
as CCTV (Closed Circuit Television) have been studied extensively. Motion
detection algorithm is mostly based on background subtraction. One main issue
in this technique is that false positives of dynamic backgrounds such as wind
shaking trees and flowing rivers might occur. In this paper, we proposed a
method to search for dynamic background region by analyzing the video and
removing false positives by re-checking false positives. The proposed method
was evaluated based on CDnet 2012/2014 dataset obtained at
"changedetection.net" site. We also compared its processing speed with other
algorithms.
| null |
http://arxiv.org/abs/1805.09277v1
|
http://arxiv.org/pdf/1805.09277v1.pdf
| null |
[
"Sang-Ha Lee",
"Soon-Chul Kwon",
"Jin-Wook Shim",
"Jeong-Eun Lim",
"Jisang Yoo"
] |
[
"Motion Detection"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/reinforcement-learning-for-heterogeneous
|
1805.09267
| null | null |
Reinforcement Learning for Heterogeneous Teams with PALO Bounds
|
We introduce reinforcement learning for heterogeneous teams in which rewards
for an agent are additively factored into local costs, stimuli unique to each
agent, and global rewards, those shared by all agents in the domain. Motivating
domains include coordination of varied robotic platforms, which incur different
costs for the same action, but share an overall goal. We present two templates
for learning in this setting with factored rewards: a generalization of
Perkins' Monte Carlo exploring starts for POMDPs to canonical MPOMDPs, with a
single policy mapping joint observations of all agents to joint actions
(MCES-MP); and another with each agent individually mapping joint observations
to their own action (MCES-FMP). We use probably approximately local optimal
(PALO) bounds to analyze sample complexity, instantiating these templates to
PALO learning. We promote sample efficiency by including a policy space pruning
technique, and evaluate the approaches on three domains of heterogeneous agents
demonstrating that MCES-FMP yields improved policies in less samples compared
to MCES-MP and a previous benchmark.
| null |
http://arxiv.org/abs/1805.09267v1
|
http://arxiv.org/pdf/1805.09267v1.pdf
| null |
[
"Roi Ceren",
"Prashant Doshi",
"Keyang He"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/collective-online-learning-of-gaussian
|
1805.09266
| null | null |
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
|
Distributed machine learning (ML) is a modern computation paradigm that
divides its workload into independent tasks that can be simultaneously achieved
by multiple machines (i.e., agents) for better scalability. However, a typical
distributed system is usually implemented with a central server that collects
data statistics from multiple independent machines operating on different
subsets of data to build a global analytic model. This centralized
communication architecture however exposes a single choke point for operational
failure and places severe bottlenecks on the server's communication and
computation capacities as it has to process a growing volume of communication
from a crowd of learning agents. To mitigate these bottlenecks, this paper
introduces a novel Collective Online Learning Gaussian Process framework for
massive distributed systems that allows each agent to build its local model,
which can be exchanged and combined efficiently with others via peer-to-peer
communication to converge on a global model of higher quality. Finally, our
empirical results consistently demonstrate the efficiency of our framework on
both synthetic and real-world datasets.
| null |
http://arxiv.org/abs/1805.09266v2
|
http://arxiv.org/pdf/1805.09266v2.pdf
| null |
[
"Trong Nghia Hoang",
"Quang Minh Hoang",
"Kian Hsiang Low",
"Jonathan How"
] |
[
"Gaussian Processes"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/learning-illuminant-estimation-from-object
|
1805.09264
| null | null |
Learning Illuminant Estimation from Object Recognition
|
In this paper we present a deep learning method to estimate the illuminant of
an image. Our model is not trained with illuminant annotations, but with the
objective of improving performance on an auxiliary task such as object
recognition. To the best of our knowledge, this is the first example of a deep
learning architecture for illuminant estimation that is trained without ground
truth illuminants. We evaluate our solution on standard datasets for color
constancy, and compare it with state of the art methods. Our proposal is shown
to outperform most deep learning methods in a cross-dataset evaluation setup,
and to present competitive results in a comparison with parametric solutions.
| null |
http://arxiv.org/abs/1805.09264v1
|
http://arxiv.org/pdf/1805.09264v1.pdf
| null |
[
"Marco Buzzelli",
"Joost Van de Weijer",
"Raimondo Schettini"
] |
[
"Color Constancy",
"Deep Learning",
"Object",
"Object Recognition"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/extraction-of-v2v-encountering-scenarios-from
|
1802.09917
| null | null |
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
|
It is necessary to thoroughly evaluate the effectiveness and safety of
Connected Vehicles (CVs) algorithm before their release and deployment. Current
evaluation approach mainly relies on simulation platform with the
single-vehicle driving model. The main drawback of it is the lack of network
realism. To overcome this problem, we extract naturalistic V2V encounters data
from the database, and then separate the primary vehicle encounter category by
clustering. A fast mining algorithm is proposed that can be applied to parallel
query for further process acceleration. 4,500 encounters are mined from a 275
GB database collected in the Safety Pilot Model Program in Ann Arbor Michigan,
USA. K-means and Dynamic Time Warping (DTW) are used in clustering. Results
show this method can quickly mine and cluster primary driving scenarios from a
large database. Our results separate the car-following, intersection and
by-passing, which are the primary category of the vehicle encounter. We
anticipate the work in the essay can become a general method to effectively
extract vehicle encounters from any existing database that contains vehicular
GPS information. What's more, the naturalistic data of different vehicle
encounters can be applied in Connected Vehicles evaluation.
| null |
http://arxiv.org/abs/1802.09917v2
|
http://arxiv.org/pdf/1802.09917v2.pdf
| null |
[
"Zhaobin Mo",
"Sisi Li",
"Diange Yang",
"Ding Zhao"
] |
[
"Clustering",
"Dynamic Time Warping"
] | 2018-02-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cleaning-up-the-neighborhood-a-full
|
1805.09247
| null | null |
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
|
Partial monitoring is a generalization of the well-known multi-armed bandit
framework where the loss is not directly observed by the learner. We complete
the classification of finite adversarial partial monitoring to include all
games, solving an open problem posed by Bartok et al. [2014]. Along the way we
simplify and improve existing algorithms and correct errors in previous
analyses. Our second contribution is a new algorithm for the class of games
studied by Bartok [2013] where we prove upper and lower regret bounds that shed
more light on the dependence of the regret on the game structure.
| null |
http://arxiv.org/abs/1805.09247v1
|
http://arxiv.org/pdf/1805.09247v1.pdf
| null |
[
"Tor Lattimore",
"Csaba Szepesvari"
] |
[
"General Classification"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/concentric-esn-assessing-the-effect-of
|
1805.09244
| null | null |
Concentric ESN: Assessing the Effect of Modularity in Cycle Reservoirs
|
The paper introduces concentric Echo State Network, an approach to design
reservoir topologies that tries to bridge the gap between deterministically
constructed simple cycle models and deep reservoir computing approaches. We
show how to modularize the reservoir into simple unidirectional and concentric
cycles with pairwise bidirectional jump connections between adjacent loops. We
provide a preliminary experimental assessment showing how concentric reservoirs
yield to superior predictive accuracy and memory capacity with respect to
single cycle reservoirs and deep reservoir models.
| null |
http://arxiv.org/abs/1805.09244v1
|
http://arxiv.org/pdf/1805.09244v1.pdf
| null |
[
"Davide Bacciu",
"Andrea Bongiorno"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/subspace-clustering-by-block-diagonal
|
1805.09243
| null | null |
Subspace Clustering by Block Diagonal Representation
|
This paper studies the subspace clustering problem. Given some data points
approximately drawn from a union of subspaces, the goal is to group these data
points into their underlying subspaces. Many subspace clustering methods have
been proposed and among which sparse subspace clustering and low-rank
representation are two representative ones. Despite the different motivations,
we observe that many existing methods own the common block diagonal property,
which possibly leads to correct clustering, yet with their proofs given case by
case. In this work, we consider a general formulation and provide a unified
theoretical guarantee of the block diagonal property. The block diagonal
property of many existing methods falls into our special case. Second, we
observe that many existing methods approximate the block diagonal
representation matrix by using different structure priors, e.g., sparsity and
low-rankness, which are indirect. We propose the first block diagonal matrix
induced regularizer for directly pursuing the block diagonal matrix. With this
regularizer, we solve the subspace clustering problem by Block Diagonal
Representation (BDR), which uses the block diagonal structure prior. The BDR
model is nonconvex and we propose an alternating minimization solver and prove
its convergence. Experiments on real datasets demonstrate the effectiveness of
BDR.
| null |
http://arxiv.org/abs/1805.09243v1
|
http://arxiv.org/pdf/1805.09243v1.pdf
| null |
[
"Canyi Lu",
"Jiashi Feng",
"Zhouchen Lin",
"Tao Mei",
"Shuicheng Yan"
] |
[
"Clustering"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/black-box-generation-of-adversarial-text
|
1801.04354
| null | null |
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
|
Although various techniques have been proposed to generate adversarial
samples for white-box attacks on text, little attention has been paid to
black-box attacks, which are more realistic scenarios. In this paper, we
present a novel algorithm, DeepWordBug, to effectively generate small text
perturbations in a black-box setting that forces a deep-learning classifier to
misclassify a text input. We employ novel scoring strategies to identify the
critical tokens that, if modified, cause the classifier to make an incorrect
prediction. Simple character-level transformations are applied to the
highest-ranked tokens in order to minimize the edit distance of the
perturbation, yet change the original classification. We evaluated DeepWordBug
on eight real-world text datasets, including text classification, sentiment
analysis, and spam detection. We compare the result of DeepWordBug with two
baselines: Random (Black-box) and Gradient (White-box). Our experimental
results indicate that DeepWordBug reduces the prediction accuracy of current
state-of-the-art deep-learning models, including a decrease of 68\% on average
for a Word-LSTM model and 48\% on average for a Char-CNN model.
|
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios.
|
http://arxiv.org/abs/1801.04354v5
|
http://arxiv.org/pdf/1801.04354v5.pdf
| null |
[
"Ji Gao",
"Jack Lanchantin",
"Mary Lou Soffa",
"Yanjun Qi"
] |
[
"Adversarial Text",
"General Classification",
"Sentiment Analysis",
"Spam detection",
"text-classification",
"Text Classification",
"Text Classification (Sentiment Analysis)"
] | 2018-01-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/highway-state-gating-for-recurrent-highway
|
1805.09238
| null | null |
Highway State Gating for Recurrent Highway Networks: improving information flow through time
|
Recurrent Neural Networks (RNNs) play a major role in the field of sequential
learning, and have outperformed traditional algorithms on many benchmarks.
Training deep RNNs still remains a challenge, and most of the state-of-the-art
models are structured with a transition depth of 2-4 layers. Recurrent Highway
Networks (RHNs) were introduced in order to tackle this issue. These have
achieved state-of-the-art performance on a few benchmarks using a depth of 10
layers. However, the performance of this architecture suffers from a
bottleneck, and ceases to improve when an attempt is made to add more layers.
In this work, we analyze the causes for this, and postulate that the main
source is the way that the information flows through time. We introduce a novel
and simple variation for the RHN cell, called Highway State Gating (HSG), which
allows adding more layers, while continuing to improve performance. By using a
gating mechanism for the state, we allow the net to "choose" whether to pass
information directly through time, or to gate it. This mechanism also allows
the gradient to back-propagate directly through time and, therefore, results in
a slightly faster convergence. We use the Penn Treebank (PTB) dataset as a
platform for empirical proof of concept. Empirical results show that the
improvement due to Highway State Gating is for all depths, and as the depth
increases, the improvement also increases.
|
Recurrent Neural Networks (RNNs) play a major role in the field of sequential learning, and have outperformed traditional algorithms on many benchmarks.
|
http://arxiv.org/abs/1805.09238v1
|
http://arxiv.org/pdf/1805.09238v1.pdf
| null |
[
"Ron Shoham",
"Haim Permuter"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cramer-wold-autoencoder
|
1805.09235
| null |
rkgwuiA9F7
|
Cramer-Wold AutoEncoder
|
We propose a new generative model, Cramer-Wold Autoencoder (CWAE). Following WAE, we directly encourage normality of the latent space. Our paper uses also the recent idea from Sliced WAE (SWAE) model, which uses one-dimensional projections as a method of verifying closeness of two distributions. The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE. We show that the Cramer-Wold metric between Gaussian mixtures is given by a simple analytic formula, which results in the removal of sampling necessary to estimate the cost function in WAE and SWAE models. As a consequence, while drastically simplifying the optimization procedure, CWAE produces samples of a matching perceptual quality to other SOTA models.
|
The crucial new ingredient is the introduction of a new (Cramer-Wold) metric in the space of densities, which replaces the Wasserstein metric used in SWAE.
|
https://arxiv.org/abs/1805.09235v3
|
https://arxiv.org/pdf/1805.09235v3.pdf
|
ICLR 2019 5
|
[
"Szymon Knop",
"Jacek Tabor",
"Przemysław Spurek",
"Igor Podolak",
"Marcin Mazur",
"Stanisław Jastrzębski"
] |
[] | 2018-05-23T00:00:00 |
https://openreview.net/forum?id=rkgwuiA9F7
|
https://openreview.net/pdf?id=rkgwuiA9F7
|
cramer-wold-autoencoder-1
| null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/segmentation-of-liver-lesions-with-reduced
|
1805.09233
| null | null |
Segmentation of Liver Lesions with Reduced Complexity Deep Models
|
We propose a computationally efficient architecture that learns to segment
lesions from CT images of the liver. The proposed architecture uses bilinear
interpolation with sub-pixel convolution at the last layer to upscale the
course feature in bottle neck architecture. Since bilinear interpolation and
sub-pixel convolution do not have any learnable parameter, our overall model is
faster and occupies less memory footprint than the traditional U-net. We
evaluate our proposed architecture on the highly competitive dataset of 2017
Liver Tumor Segmentation (LiTS) Challenge. Our method achieves competitive
results while reducing the number of learnable parameters roughly by a factor
of 13.8 compared to the original UNet model.
| null |
http://arxiv.org/abs/1805.09233v1
|
http://arxiv.org/pdf/1805.09233v1.pdf
| null |
[
"Ram Krishna Pandey",
"Aswin Vasan",
"A. G. Ramakrishnan"
] |
[
"Tumor Segmentation"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/symmslic-symmetry-aware-superpixel
|
1805.09232
| null | null |
SymmSLIC: Symmetry Aware Superpixel Segmentation and its Applications
|
Over-segmentation of an image into superpixels has become a useful tool for
solving various problems in image processing and computer vision. Reflection
symmetry is quite prevalent in both natural and man-made objects and is an
essential cue in understanding and grouping the objects in natural scenes.
Existing algorithms for estimating superpixels do not preserve the reflection
symmetry of an object which leads to different sizes and shapes of superpixels
across the symmetry axis. In this work, we propose an algorithm to over-segment
an image through the propagation of reflection symmetry evident at the pixel
level to superpixel boundaries. In order to achieve this goal, we first find
the reflection symmetry in the image and represent it by a set of pairs of
pixels which are mirror reflections of each other. We partition the image into
superpixels while preserving this reflection symmetry through an iterative
algorithm. We compare the proposed method with state-of-the-art superpixel
generation methods and show the effectiveness in preserving the size and shape
of superpixel boundaries across the reflection symmetry axes. We also present
two applications, symmetry axes detection and unsupervised symmetric object
segmentation, to illustrate the effectiveness of the proposed approach.
| null |
http://arxiv.org/abs/1805.09232v2
|
http://arxiv.org/pdf/1805.09232v2.pdf
| null |
[
"Rajendra Nagar",
"Shanmuganathan Raman"
] |
[
"Semantic Segmentation",
"Superpixels"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/improving-lstm-ctc-based-asr-performance-in
|
1707.00722
| null | null |
Improving LSTM-CTC based ASR performance in domains with limited training data
|
This paper addresses the observed performance gap between automatic speech
recognition (ASR) systems based on Long Short Term Memory (LSTM) neural
networks trained with the connectionist temporal classification (CTC) loss
function and systems based on hybrid Deep Neural Networks (DNNs) trained with
the cross entropy (CE) loss function on domains with limited data. We step
through a number of experiments that show incremental improvements on a
baseline EESEN toolkit based LSTM-CTC ASR system trained on the Librispeech
100hr (train-clean-100) corpus. Our results show that with effective
combination of data augmentation and regularization, a LSTM-CTC based system
can exceed the performance of a strong Kaldi based baseline trained on the same
data.
|
This paper addresses the observed performance gap between automatic speech recognition (ASR) systems based on Long Short Term Memory (LSTM) neural networks trained with the connectionist temporal classification (CTC) loss function and systems based on hybrid Deep Neural Networks (DNNs) trained with the cross entropy (CE) loss function on domains with limited data.
|
http://arxiv.org/abs/1707.00722v2
|
http://arxiv.org/pdf/1707.00722v2.pdf
| null |
[
"Jayadev Billa"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"Data Augmentation",
"General Classification",
"speech-recognition",
"Speech Recognition"
] | 2017-07-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/monte-carlo-tree-search-for-asymmetric-trees
|
1805.09218
| null | null |
Monte Carlo Tree Search for Asymmetric Trees
|
We present an extension of Monte Carlo Tree Search (MCTS) that strongly
increases its efficiency for trees with asymmetry and/or loops. Asymmetric
termination of search trees introduces a type of uncertainty for which the
standard upper confidence bound (UCB) formula does not account. Our first
algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree
structure uncertainty and leverages it for exploration in a modified UCB
formula. Results show vastly improved efficiency in a well-known asymmetric
domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about
asymmetric termination to the presence of loops in the tree, where the same
state appears multiple times in a single trace. An extension to our algorithm
(MCTS-T+), which in addition to non-stochasticity assumes full state
observability, further increases search efficiency for domains with loops as
well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates
that our algorithms always perform better than or at least equivalent to
standard MCTS, and could be first-choice tree search algorithms for
non-stochastic, fully-observable environments.
|
Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account.
|
http://arxiv.org/abs/1805.09218v1
|
http://arxiv.org/pdf/1805.09218v1.pdf
| null |
[
"Thomas M. Moerland",
"Joost Broekens",
"Aske Plaat",
"Catholijn M. Jonker"
] |
[
"Atari Games",
"OpenAI Gym"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tight-bounds-for-collaborative-pac-learning
|
1805.09217
| null | null |
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
|
We study the collaborative PAC learning problem recently proposed in Blum et
al.~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target
function collaboratively, such that the learned function approximates the
target function well on all players' distributions simultaneously. The quality
of the collaborative learning algorithm is measured by the ratio between the
sample complexity of the algorithm and that of the learning algorithm for a
single distribution (called the overhead). We obtain a collaborative learning
algorithm with overhead $O(\ln k)$, improving the one with overhead $O(\ln^2
k)$ in \cite{BHPQ17}. We also show that an $\Omega(\ln k)$ overhead is
inevitable when $k$ is polynomial bounded by the VC dimension of the hypothesis
class. Finally, our experimental study has demonstrated the superiority of our
algorithm compared with the one in Blum et al. on real-world datasets.
| null |
http://arxiv.org/abs/1805.09217v2
|
http://arxiv.org/pdf/1805.09217v2.pdf
|
NeurIPS 2018 12
|
[
"Jiecao Chen",
"Qin Zhang",
"Yuan Zhou"
] |
[
"PAC learning"
] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7618-tight-bounds-for-collaborative-pac-learning-via-multiplicative-weights
|
http://papers.nips.cc/paper/7618-tight-bounds-for-collaborative-pac-learning-via-multiplicative-weights.pdf
|
tight-bounds-for-collaborative-pac-learning-1
| null |
[] |
https://paperswithcode.com/paper/a-unified-framework-for-training-neural
|
1805.09214
| null | null |
A Unified Framework for Training Neural Networks
|
The lack of mathematical tractability of Deep Neural Networks (DNNs) has
hindered progress towards having a unified convergence analysis of training
algorithms, in the general setting. We propose a unified optimization framework
for training different types of DNNs, and establish its convergence for
arbitrary loss, activation, and regularization functions, assumed to be smooth.
We show that framework generalizes well-known first- and second-order training
methods, and thus allows us to show the convergence of these methods for
various DNN architectures and learning tasks, as a special case of our
approach. We discuss some of its applications in training various DNN
architectures (e.g., feed-forward, convolutional, linear networks), to
regression and classification tasks.
| null |
http://arxiv.org/abs/1805.09214v1
|
http://arxiv.org/pdf/1805.09214v1.pdf
| null |
[
"Hadi Ghauch",
"Hossein Shokri-Ghadikolaei",
"Carlo Fischione",
"Mikael Skoglund"
] |
[
"General Classification",
"regression"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-latent-variable-structured
|
1805.09213
| null | null |
Learning latent variable structured prediction models with Gaussian perturbations
|
The standard margin-based structured prediction commonly uses a maximum loss
over all possible structured outputs. The large-margin formulation including
latent variables not only results in a non-convex formulation but also
increases the search space by a factor of the size of the latent space. Recent
work has proposed the use of the maximum loss over random structured outputs
sampled independently from some proposal distribution, with theoretical
guarantees. We extend this work by including latent variables. We study a new
family of loss functions under Gaussian perturbations and analyze the effect of
the latent space on the generalization bounds. We show that the non-convexity
of learning with latent variables originates naturally, as it relates to a
tight upper bound of the Gibbs decoder distortion with respect to the latent
space. Finally, we provide a formulation using random samples that produces a
tighter upper bound of the Gibbs decoder distortion up to a statistical
accuracy, which enables a faster evaluation of the objective function. We
illustrate the method with synthetic experiments and a computer vision
application.
| null |
http://arxiv.org/abs/1805.09213v1
|
http://arxiv.org/pdf/1805.09213v1.pdf
|
NeurIPS 2018 12
|
[
"Kevin Bello",
"Jean Honorio"
] |
[
"Decoder",
"Generalization Bounds",
"Prediction",
"Structured Prediction"
] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7577-learning-latent-variable-structured-prediction-models-with-gaussian-perturbations
|
http://papers.nips.cc/paper/7577-learning-latent-variable-structured-prediction-models-with-gaussian-perturbations.pdf
|
learning-latent-variable-structured-1
| null |
[] |
https://paperswithcode.com/paper/learning-pose-specific-representations-by
|
1804.03390
| null | null |
Learning Pose Specific Representations by Predicting Different Views
|
The labeled data required to learn pose estimation for articulated objects is
difficult to provide in the desired quantity, realism, density, and accuracy.
To address this issue, we develop a method to learn representations, which are
very specific for articulated poses, without the need for labeled training
data. We exploit the observation that the object pose of a known object is
predictive for the appearance in any known view. That is, given only the pose
and shape parameters of a hand, the hand's appearance from any viewpoint can be
approximated. To exploit this observation, we train a model that -- given input
from one view -- estimates a latent representation, which is trained to be
predictive for the appearance of the object when captured from another
viewpoint. Thus, the only necessary supervision is the second view. The
training process of this model reveals an implicit pose representation in the
latent space. Importantly, at test time the pose representation can be inferred
using only a single view. In qualitative and quantitative experiments we show
that the learned representations capture detailed pose information. Moreover,
when training the proposed method jointly with labeled and unlabeled data, it
consistently surpasses the performance of its fully supervised counterpart,
while reducing the amount of needed labeled samples by at least one order of
magnitude.
|
To exploit this observation, we train a model that -- given input from one view -- estimates a latent representation, which is trained to be predictive for the appearance of the object when captured from another viewpoint.
|
http://arxiv.org/abs/1804.03390v2
|
http://arxiv.org/pdf/1804.03390v2.pdf
|
CVPR 2018 6
|
[
"Georg Poier",
"David Schinagl",
"Horst Bischof"
] |
[
"Hand Pose Estimation",
"Object",
"Pose Estimation"
] | 2018-04-10T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Poier_Learning_Pose_Specific_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Poier_Learning_Pose_Specific_CVPR_2018_paper.pdf
|
learning-pose-specific-representations-by-1
| null |
[] |
https://paperswithcode.com/paper/abstractive-text-classification-using
|
1805.07745
| null | null |
Abstractive Text Classification Using Sequence-to-convolution Neural Networks
|
We propose a new deep neural network model and its training scheme for text classification. Our model Sequence-to-convolution Neural Networks(Seq2CNN) consists of two blocks: Sequential Block that summarizes input texts and Convolution Block that receives summary of input and classifies it to a label. Seq2CNN is trained end-to-end to classify various-length texts without preprocessing inputs into fixed length. We also present Gradual Weight Shift(GWS) method that stabilizes training. GWS is applied to our model's loss function. We compared our model with word-based TextCNN trained with different data preprocessing methods. We obtained significant improvement in classification accuracy over word-based TextCNN without any ensemble or data augmentation.
|
Seq2CNN is trained end-to-end to classify various-length texts without preprocessing inputs into fixed length.
|
https://arxiv.org/abs/1805.07745v6
|
https://arxiv.org/pdf/1805.07745v6.pdf
| null |
[
"Taehoon Kim",
"Jihoon Yang"
] |
[
"Classification",
"Data Augmentation",
"General Classification",
"text-classification",
"Text Classification"
] | 2018-05-20T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/how-much-does-a-word-weigh-weighting-word
|
1805.09209
| null | null |
How much does a word weigh? Weighting word embeddings for word sense induction
|
The paper describes our participation in the first shared task on word sense
induction and disambiguation for the Russian language RUSSE'2018 (Panchenko et
al., 2018). For each of several dozens of ambiguous words, the participants
were asked to group text fragments containing it according to the senses of
this word, which were not provided beforehand, therefore the "induction" part
of the task. For instance, a word "bank" and a set of text fragments (also
known as "contexts") in which this word occurs, e.g. "bank is a financial
institution that accepts deposits" and "river bank is a slope beside a body of
water" were given. A participant was asked to cluster such contexts in the
unknown in advance number of clusters corresponding to, in this case, the
"company" and the "area" senses of the word "bank". The organizers proposed
three evaluation datasets of varying complexity and text genres based
respectively on texts of Wikipedia, Web pages, and a dictionary of the Russian
language. We present two experiments: a positive and a negative one, based
respectively on clustering of contexts represented as a weighted average of
word embeddings and on machine translation using two state-of-the-art
production neural machine translation systems. Our team showed the second best
result on two datasets and the third best result on the remaining one dataset
among 18 participating teams. We managed to substantially outperform
competitive state-of-the-art baselines from the previous years based on sense
embeddings.
| null |
http://arxiv.org/abs/1805.09209v2
|
http://arxiv.org/pdf/1805.09209v2.pdf
| null |
[
"Nikolay Arefyev",
"Pavel Ermolaev",
"Alexander Panchenko"
] |
[
"Clustering",
"Machine Translation",
"Translation",
"Word Embeddings",
"Word Sense Induction"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pushing-the-bounds-of-dropout
|
1805.09208
| null |
rklwwo05Ym
|
Pushing the bounds of dropout
|
We show that dropout training is best understood as performing MAP estimation
concurrently for a family of conditional models whose objectives are themselves
lower bounded by the original dropout objective. This discovery allows us to
pick any model from this family after training, which leads to a substantial
improvement on regularisation-heavy language modelling. The family includes
models that compute a power mean over the sampled dropout masks, and their less
stochastic subvariants with tighter and higher lower bounds than the fully
stochastic dropout objective. We argue that since the deterministic
subvariant's bound is equal to its objective, and the highest amongst these
models, the predominant view of it as a good approximation to MC averaging is
misleading. Rather, deterministic dropout is the best available approximation
to the true objective.
|
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the original dropout objective.
|
http://arxiv.org/abs/1805.09208v2
|
http://arxiv.org/pdf/1805.09208v2.pdf
|
ICLR 2019 5
|
[
"Gábor Melis",
"Charles Blundell",
"Tomáš Kočiský",
"Karl Moritz Hermann",
"Chris Dyer",
"Phil Blunsom"
] |
[
"Language Modelling"
] | 2018-05-23T00:00:00 |
https://openreview.net/forum?id=rklwwo05Ym
|
https://openreview.net/pdf?id=rklwwo05Ym
|
pushing-the-bounds-of-dropout-1
| null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
}
] |
https://paperswithcode.com/paper/attributes-in-multiple-facial-images
|
1805.09203
| null | null |
Attributes in Multiple Facial Images
|
Facial attribute recognition is conventionally computed from a single image.
In practice, each subject may have multiple face images. Taking the eye size as
an example, it should not change, but it may have different estimation in
multiple images, which would make a negative impact on face recognition. Thus,
how to compute these attributes corresponding to each subject rather than each
single image is a profound work. To address this question, we deploy deep
training for facial attributes prediction, and we explore the inconsistency
issue among the attributes computed from each single image. Then, we develop
two approaches to address the inconsistency issue. Experimental results show
that the proposed methods can handle facial attribute estimation on either
multiple still images or video frames, and can correct the incorrectly
annotated labels. The experiments are conducted on two large public databases
with annotations of facial attributes.
| null |
http://arxiv.org/abs/1805.09203v1
|
http://arxiv.org/pdf/1805.09203v1.pdf
| null |
[
"Xudong Liu",
"Guodong Guo"
] |
[
"Attribute",
"Face Recognition"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fidelity-weighted-learning
|
1711.02799
| null |
B1X0mzZCW
|
Fidelity-Weighted Learning
|
Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental quality versus-quantity trade-off in the learning process. Do we
learn from the small amount of high-quality data or the potentially large
amount of weakly-labeled data? We argue that if the learner could somehow know
and take the label-quality into account when learning the data representation,
we could get the best of both worlds. To this end, we propose
"fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach
for training deep neural networks using weakly-labeled data. FWL modulates the
parameter updates to a student network (trained on the task we care about) on a
per-sample basis according to the posterior confidence of its label-quality
estimated by a teacher (who has access to the high-quality labels). Both
student and teacher are learned from the data. We evaluate FWL on two tasks in
information retrieval and natural language processing where we outperform
state-of-the-art alternative semi-supervised methods, indicating that our
approach makes better use of strong and weak labels, and leads to better
task-dependent data representations.
| null |
http://arxiv.org/abs/1711.02799v2
|
http://arxiv.org/pdf/1711.02799v2.pdf
|
ICLR 2018 1
|
[
"Mostafa Dehghani",
"Arash Mehrjou",
"Stephan Gouws",
"Jaap Kamps",
"Bernhard Schölkopf"
] |
[
"Ad-Hoc Information Retrieval",
"Information Retrieval",
"Retrieval"
] | 2017-11-08T00:00:00 |
https://openreview.net/forum?id=B1X0mzZCW
|
https://openreview.net/pdf?id=B1X0mzZCW
|
fidelity-weighted-learning-1
| null |
[] |
https://paperswithcode.com/paper/towards-the-first-adversarially-robust-neural
|
1805.09190
| null |
S1EHOsC9tX
|
Towards the first adversarially robust neural network model on MNIST
|
Despite much effort, deep neural networks remain highly susceptible to tiny
input perturbations and even for MNIST, one of the most common toy datasets in
computer vision, no neural network model exists for which adversarial
perturbations are large and make semantic sense to humans. We show that even
the widely recognized and by far most successful defense by Madry et al. (1)
overfits on the L-infinity metric (it's highly susceptible to L2 and L0
perturbations), (2) classifies unrecognizable images with high certainty, (3)
performs not much better than simple input binarization and (4) features
adversarial perturbations that make little sense to humans. These results
suggest that MNIST is far from being solved in terms of adversarial robustness.
We present a novel robust classification model that performs analysis by
synthesis using learned class-conditional data distributions. We derive bounds
on the robustness and go to great length to empirically evaluate our model
using maximally effective adversarial attacks by (a) applying decision-based,
score-based, gradient-based and transfer-based attacks for several different Lp
norms, (b) by designing a new attack that exploits the structure of our
defended model and (c) by devising a novel decision-based attack that seeks to
minimize the number of perturbed pixels (L0). The results suggest that our
approach yields state-of-the-art robustness on MNIST against L0, L2 and
L-infinity perturbations and we demonstrate that most adversarial examples are
strongly perturbed towards the perceptual boundary between the original and the
adversarial class.
|
Despite much effort, deep neural networks remain highly susceptible to tiny input perturbations and even for MNIST, one of the most common toy datasets in computer vision, no neural network model exists for which adversarial perturbations are large and make semantic sense to humans.
|
http://arxiv.org/abs/1805.09190v3
|
http://arxiv.org/pdf/1805.09190v3.pdf
|
ICLR 2019 5
|
[
"Lukas Schott",
"Jonas Rauber",
"Matthias Bethge",
"Wieland Brendel"
] |
[
"Adversarial Robustness",
"Binarization",
"Robust classification"
] | 2018-05-23T00:00:00 |
https://openreview.net/forum?id=S1EHOsC9tX
|
https://openreview.net/pdf?id=S1EHOsC9tX
|
towards-the-first-adversarially-robust-neural-1
| null |
[] |
https://paperswithcode.com/paper/efficient-online-algorithms-for-fast-rate
|
1805.09174
| null | null |
Efficient online algorithms for fast-rate regret bounds under sparsity
|
We consider the online convex optimization problem. In the setting of
arbitrary sequences and finite set of parameters, we establish a new fast-rate
quantile regret bound. Then we investigate the optimization into the L1-ball by
discretizing the parameter space. Our algorithm is projection free and we
propose an efficient solution by restarting the algorithm on adaptive
discretization grids. In the adversarial setting, we develop an algorithm that
achieves several rates of convergence with different dependencies on the
sparsity of the objective. In the i.i.d. setting, we establish new risk bounds
that are adaptive to the sparsity of the problem and to the regularity of the
risk (ranging from a rate 1 / $\sqrt T$ for general convex risk to 1 /T for
strongly convex risk). These results generalize previous works on sparse online
learning. They are obtained under a weak assumption on the risk
({\L}ojasiewicz's assumption) that allows multiple optima which is crucial when
dealing with degenerate situations.
| null |
http://arxiv.org/abs/1805.09174v1
|
http://arxiv.org/pdf/1805.09174v1.pdf
|
NeurIPS 2018 12
|
[
"Pierre Gaillard",
"Olivier Wintenberger"
] |
[] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7934-efficient-online-algorithms-for-fast-rate-regret-bounds-under-sparsity
|
http://papers.nips.cc/paper/7934-efficient-online-algorithms-for-fast-rate-regret-bounds-under-sparsity.pdf
|
efficient-online-algorithms-for-fast-rate-1
| null |
[] |
https://paperswithcode.com/paper/matrix-co-completion-for-multi-label
|
1805.09156
| null | null |
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
|
We consider a challenging multi-label classification problem where both
feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing
method concatenated $\X$ and $\Y$ as $[\X; \Y]$ and applied a matrix completion
(MC) method to fill the missing entries, under the assumption that $[\X; \Y]$
is of low-rank. However, since entries of $\Y$ take binary values in the
multi-label setting, it is unlikely that $\Y$ is of low-rank. Moreover, such
assumption implies a linear relationship between $\X$ and $\Y$ which may not
hold in practice. In this paper, we consider a latent matrix $\Z$ that produces
the probability $\sigma(Z_{ij})$ of generating label $Y_{ij}$, where
$\sigma(\cdot)$ is nonlinear. Considering label correlation, we assume $[\X;
\Z]$ is of low-rank, and propose an MC algorithm based on subgradient descent
named co-completion (COCO) motivated by elastic net and one-bit MC. We give a
theoretical bound on the recovery effect of COCO and demonstrate its practical
usefulness through experiments.
| null |
http://arxiv.org/abs/1805.09156v1
|
http://arxiv.org/pdf/1805.09156v1.pdf
| null |
[
"Miao Xu",
"Gang Niu",
"Bo Han",
"Ivor W. Tsang",
"Zhi-Hua Zhou",
"Masashi Sugiyama"
] |
[
"General Classification",
"Matrix Completion",
"Multi-Label Classification",
"MUlTI-LABEL-ClASSIFICATION"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rdf2vec-based-classification-of-ontology
|
1805.09145
| null | null |
RDF2Vec-based Classification of Ontology Alignment Changes
|
When ontologies cover overlapping topics, the overlap can be represented
using ontology alignments. These alignments need to be continuously adapted to
changing ontologies. Especially for large ontologies this is a costly task
often consisting of manual work. Finding changes that do not lead to an
adaption of the alignment can potentially make this process significantly
easier. This work presents an approach to finding these changes based on RDF
embeddings and common classification techniques. To examine the feasibility of
this approach, an evaluation on a real-world dataset is presented. In this
evaluation, the best classifiers reached a precision of 0.8.
| null |
http://arxiv.org/abs/1805.09145v1
|
http://arxiv.org/pdf/1805.09145v1.pdf
| null |
[
"Matthias Jurisch",
"Bodo Igler"
] |
[
"Classification",
"General Classification"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dataflow-matrix-machines-and-v-values-a
|
1712.07447
| null | null |
Dataflow Matrix Machines and V-values: a Bridge between Programs and Neural Nets
|
1) Dataflow matrix machines (DMMs) generalize neural nets by replacing
streams of numbers with linear streams (streams supporting linear
combinations), allowing arbitrary input and output arities for activation
functions, countable-sized networks with finite dynamically changeable active
part capable of unbounded growth, and a very expressive self-referential
mechanism.
2) DMMs are suitable for general-purpose programming, while retaining the key
property of recurrent neural networks: programs are expressed via matrices of
real numbers, and continuous changes to those matrices produce arbitrarily
small variations in the associated programs.
3) Spaces of V-values (vector-like elements based on nested maps) are
particularly useful, enabling DMMs with variadic activation functions and
conveniently representing conventional data structures.
|
1) Dataflow matrix machines (DMMs) generalize neural nets by replacing streams of numbers with linear streams (streams supporting linear combinations), allowing arbitrary input and output arities for activation functions, countable-sized networks with finite dynamically changeable active part capable of unbounded growth, and a very expressive self-referential mechanism.
|
http://arxiv.org/abs/1712.07447v2
|
http://arxiv.org/pdf/1712.07447v2.pdf
| null |
[
"Michael Bukatin",
"Jon Anthony"
] |
[] | 2017-12-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/probabilistic-riemannian-submanifold-learning
|
1805.09122
| null | null |
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
|
Latent variable models (LVMs) learn probabilistic models of data manifolds
lying in an \emph{ambient} Euclidean space. In a number of applications, a
priori known spatial constraints can shrink the ambient space into a
considerably smaller manifold. Additionally, in these applications the
Euclidean geometry might induce a suboptimal similarity measure, which could be
improved by choosing a different metric. Euclidean models ignore such
information and assign probability mass to data points that can never appear as
data, and vastly different likelihoods to points that are similar under the
desired metric. We propose the wrapped Gaussian process latent variable model
(WGPLVM), that extends Gaussian process latent variable models to take values
strictly on a given ambient Riemannian manifold, making the model blind to
impossible data points. This allows non-linear, probabilistic inference of
low-dimensional Riemannian submanifolds from data. Our evaluation on diverse
datasets show that we improve performance on several tasks, including encoding,
visualization and uncertainty quantification.
| null |
http://arxiv.org/abs/1805.09122v2
|
http://arxiv.org/pdf/1805.09122v2.pdf
| null |
[
"Anton Mallasto",
"Søren Hauberg",
"Aasa Feragen"
] |
[
"Uncertainty Quantification"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/selecting-machine-translated-data-for-quick
|
1805.09119
| null | null |
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System
|
This paper investigates the use of Machine Translation (MT) to bootstrap a
Natural Language Understanding (NLU) system for a new language for the use case
of a large-scale voice-controlled device. The goal is to decrease the cost and
time needed to get an annotated corpus for the new language, while still having
a large enough coverage of user requests. Different methods of filtering MT
data in order to keep utterances that improve NLU performance and
language-specific post-processing methods are investigated. These methods are
tested in a large-scale NLU task with translating around 10 millions training
utterances from English to German. The results show a large improvement for
using MT data over a grammar-based and over an in-house data collection
baseline, while reducing the manual effort greatly. Both filtering and
post-processing approaches improve results further.
| null |
http://arxiv.org/abs/1805.09119v1
|
http://arxiv.org/pdf/1805.09119v1.pdf
|
NAACL 2018 6
|
[
"Judith Gaspers",
"Penny Karanasou",
"Rajen Chatterjee"
] |
[
"Machine Translation",
"Natural Language Understanding",
"Translation"
] | 2018-05-23T00:00:00 |
https://aclanthology.org/N18-3017
|
https://aclanthology.org/N18-3017.pdf
|
selecting-machine-translated-data-for-quick-1
| null |
[] |
https://paperswithcode.com/paper/optimal-transport-for-structured-data-with
|
1805.09114
| null | null |
Optimal Transport for structured data with application on graphs
|
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space. We consider a new transportation distance (i.e. that minimizes a total cost of transporting probability masses) that unveils the geometric nature of the structured objects space. Unlike Wasserstein or Gromov-Wasserstein metrics that focus solely and respectively on features (by considering a metric in the feature space) or structure (by seeing structure as a metric space), our new distance exploits jointly both information, and is consequently called Fused Gromov-Wasserstein (FGW). After discussing its properties and computational aspects, we show results on a graph classification task, where our method outperforms both graph kernels and deep graph convolutional networks. Exploiting further on the metric properties of FGW, interesting geometric objects such as Fr\'echet means or barycenters of graphs are illustrated and discussed in a clustering context.
|
This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.
|
https://arxiv.org/abs/1805.09114v3
|
https://arxiv.org/pdf/1805.09114v3.pdf
| null |
[
"Titouan Vayer",
"Laetitia Chapel",
"Rémi Flamary",
"Romain Tavenard",
"Nicolas Courty"
] |
[
"Clustering",
"Graph Classification",
"Graph Clustering",
"Time Series Analysis"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hyperbolic-neural-networks
|
1805.09112
| null | null |
Hyperbolic Neural Networks
|
Hyperbolic spaces have recently gained momentum in the context of machine
learning due to their high capacity and tree-likeliness properties. However,
the representational power of hyperbolic geometry is not yet on par with
Euclidean geometry, mostly because of the absence of corresponding hyperbolic
neural network layers. This makes it hard to use hyperbolic embeddings in
downstream tasks. Here, we bridge this gap in a principled manner by combining
the formalism of M\"obius gyrovector spaces with the Riemannian geometry of the
Poincar\'e model of hyperbolic spaces. As a result, we derive hyperbolic
versions of important deep learning tools: multinomial logistic regression,
feed-forward and recurrent neural networks such as gated recurrent units. This
allows to embed sequential data and perform classification in the hyperbolic
space. Empirically, we show that, even if hyperbolic optimization tools are
limited, hyperbolic sentence embeddings either outperform or are on par with
their Euclidean variants on textual entailment and noisy-prefix recognition
tasks.
|
However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.
|
http://arxiv.org/abs/1805.09112v2
|
http://arxiv.org/pdf/1805.09112v2.pdf
|
NeurIPS 2018 12
|
[
"Octavian-Eugen Ganea",
"Gary Bécigneul",
"Thomas Hofmann"
] |
[
"Graph Representation Learning",
"Natural Language Inference",
"Sentence",
"Sentence Embeddings"
] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7780-hyperbolic-neural-networks
|
http://papers.nips.cc/paper/7780-hyperbolic-neural-networks.pdf
|
hyperbolic-neural-networks-1
| null |
[] |
https://paperswithcode.com/paper/bayesian-alignments-of-warped-multi-output
|
1710.02766
| null | null |
Bayesian Alignments of Warped Multi-Output Gaussian Processes
|
We propose a novel Bayesian approach to modelling nonlinear alignments of
time series based on latent shared information. We apply the method to the
real-world problem of finding common structure in the sensor data of wind
turbines introduced by the underlying latent and turbulent wind field. The
proposed model allows for both arbitrary alignments of the inputs and
non-parametric output warpings to transform the observations. This gives rise
to multiple deep Gaussian process models connected via latent generating
processes. We present an efficient variational approximation based on nested
variational compression and show how the model can be used to extract shared
information between dependent time series, recovering an interpretable
functional decomposition of the learning problem. We show results for an
artificial data set and real-world data of two wind turbines.
| null |
http://arxiv.org/abs/1710.02766v3
|
http://arxiv.org/pdf/1710.02766v3.pdf
|
NeurIPS 2018 12
|
[
"Markus Kaiser",
"Clemens Otte",
"Thomas Runkler",
"Carl Henrik Ek"
] |
[
"Gaussian Processes",
"Time Series",
"Time Series Analysis"
] | 2017-10-08T00:00:00 |
http://papers.nips.cc/paper/7931-bayesian-alignments-of-warped-multi-output-gaussian-processes
|
http://papers.nips.cc/paper/7931-bayesian-alignments-of-warped-multi-output-gaussian-processes.pdf
|
bayesian-alignments-of-warped-multi-output-1
| null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/image-restoration-by-estimating-frequency
|
1805.09097
| null | null |
Image Restoration by Estimating Frequency Distribution of Local Patches
|
In this paper, we propose a method to solve the image restoration problem,
which tries to restore the details of a corrupted image, especially due to the
loss caused by JPEG compression. We have treated an image in the frequency
domain to explicitly restore the frequency components lost during image
compression. In doing so, the distribution in the frequency domain is learned
using the cross entropy loss. Unlike recent approaches, we have reconstructed
the details of an image without using the scheme of adversarial training.
Rather, the image restoration problem is treated as a classification problem to
determine the frequency coefficient for each frequency band in an image patch.
In this paper, we show that the proposed method effectively restores a
JPEG-compressed image with more detailed high frequency components, making the
restored image more vivid.
| null |
http://arxiv.org/abs/1805.09097v1
|
http://arxiv.org/pdf/1805.09097v1.pdf
|
CVPR 2018 6
|
[
"Jaeyoung Yoo",
"Sang-ho Lee",
"Nojun Kwak"
] |
[
"General Classification",
"Image Compression",
"Image Restoration"
] | 2018-05-23T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Yoo_Image_Restoration_by_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Yoo_Image_Restoration_by_CVPR_2018_paper.pdf
|
image-restoration-by-estimating-frequency-1
| null |
[] |
https://paperswithcode.com/paper/excitation-dropout-encouraging-plasticity-in
|
1805.09092
| null |
H1xQSjCqFQ
|
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
|
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths. In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout. In essence, we dropout with higher probability those neurons which contribute more to decision making at training time. This approach penalizes high saliency neurons that are most relevant for model prediction, i.e. those having stronger evidence. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization, resulting in a plasticity-like behavior, a characteristic of human brains too. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression using several metrics over four image/video recognition benchmarks.
|
In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.
|
https://arxiv.org/abs/1805.09092v3
|
https://arxiv.org/pdf/1805.09092v3.pdf
| null |
[
"Andrea Zunino",
"Sarah Adel Bargal",
"Pietro Morerio",
"Jianming Zhang",
"Stan Sclaroff",
"Vittorio Murino"
] |
[
"Decision Making",
"Video Recognition"
] | 2018-05-23T00:00:00 |
https://openreview.net/forum?id=H1xQSjCqFQ
|
https://openreview.net/pdf?id=H1xQSjCqFQ
| null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
}
] |
https://paperswithcode.com/paper/neural-networks-for-post-processing-ensemble
|
1805.09091
| null | null |
Neural networks for post-processing ensemble weather forecasts
|
Ensemble weather predictions require statistical post-processing of
systematic errors to obtain reliable and accurate probabilistic forecasts.
Traditionally, this is accomplished with distributional regression models in
which the parameters of a predictive distribution are estimated from a training
period. We propose a flexible alternative based on neural networks that can
incorporate nonlinear relationships between arbitrary predictor variables and
forecast distribution parameters that are automatically learned in a
data-driven way rather than requiring pre-specified link functions. In a case
study of 2-meter temperature forecasts at surface stations in Germany, the
neural network approach significantly outperforms benchmark post-processing
methods while being computationally more affordable. Key components to this
improvement are the use of auxiliary predictor variables and station-specific
information with the help of embeddings. Furthermore, the trained neural
network can be used to gain insight into the importance of meteorological
variables thereby challenging the notion of neural networks as uninterpretable
black boxes. Our approach can easily be extended to other statistical
post-processing and forecasting problems. We anticipate that recent advances in
deep learning combined with the ever-increasing amounts of model and
observation data will transform the post-processing of numerical weather
forecasts in the coming decade.
|
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.
|
http://arxiv.org/abs/1805.09091v1
|
http://arxiv.org/pdf/1805.09091v1.pdf
| null |
[
"Stephan Rasp",
"Sebastian Lerch"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-parameter-estimation-of-fractional-order
|
1805.08680
| null | null |
A Parameter Estimation of Fractional Order Grey Model Based on Adaptive Dynamic Cat Swarm Algorithm
|
In this paper, we utilize ADCSO (Adaptive Dynamic Cat Swarm Optimization) to
estimate the parameters of Fractional Order Grey Model. The parameters of
Fractional Order Grey Model affect the prediction accuracy of the model. In
order to solve the problem that general swarm intelligence algorithms easily
fall into the local optimum and optimize the accuracy of the model, ADCSO is
utilized to reduce the error of the model. Experimental results for the data of
container throughput of Wuhan Port and marine capture productions of Zhejiang
Province show that the different parameter values affect the prediction
results. The parameters estimated by ADCSO make the prediction error of the
model smaller and the convergence speed higher, and it is not easy to fall into
the local convergence compared with PSO (Particle Swarm Optimization) and LSM
(Least Square Method). The feasibility and advantage of ADCSO for the parameter
estimation of Fractional Order Grey Model are verified.
| null |
http://arxiv.org/abs/1805.08680v2
|
http://arxiv.org/pdf/1805.08680v2.pdf
| null |
[
"Binyan Lin",
"Fei Gao",
"Meng Wang",
"Yuyao Xiong",
"Ansheng Li"
] |
[
"parameter estimation",
"Prediction"
] | 2018-05-22T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/constrained-graph-variational-autoencoders
|
1805.09076
| null | null |
Constrained Graph Variational Autoencoders for Molecule Design
|
Graphs are ubiquitous data structures for representing interactions between
entities. With an emphasis on the use of graphs to represent chemical
molecules, we explore the task of learning to generate graphs that conform to a
distribution observed in training data. We propose a variational autoencoder
model in which both encoder and decoder are graph-structured. Our decoder
assumes a sequential ordering of graph extension steps and we discuss and
analyze design choices that mitigate the potential downsides of this
linearization. Experiments compare our approach with a wide range of baselines
on the molecule generation task and show that our method is more successful at
matching the statistics of the original dataset on semantically important
metrics. Furthermore, we show that by using appropriate shaping of the latent
space, our model allows us to design molecules that are (locally) optimal in
desired properties.
|
Graphs are ubiquitous data structures for representing interactions between entities.
|
http://arxiv.org/abs/1805.09076v2
|
http://arxiv.org/pdf/1805.09076v2.pdf
|
NeurIPS 2018 12
|
[
"Qi Liu",
"Miltiadis Allamanis",
"Marc Brockschmidt",
"Alexander L. Gaunt"
] |
[
"Decoder"
] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/8005-constrained-graph-variational-autoencoders-for-molecule-design
|
http://papers.nips.cc/paper/8005-constrained-graph-variational-autoencoders-for-molecule-design.pdf
|
constrained-graph-variational-autoencoders-1
| null |
[] |
https://paperswithcode.com/paper/reinforcement-learning-to-rank-in-e-commerce
|
1803.00710
| null | null |
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
|
In e-commerce platforms such as Amazon and TaoBao, ranking items in a search
session is a typical multi-step decision-making problem. Learning to rank (LTR)
methods have been widely applied to ranking problems. However, such methods
often consider different ranking steps in a session to be independent, which
conversely may be highly correlated to each other. For better utilizing the
correlation between different ranking steps, in this paper, we propose to use
reinforcement learning (RL) to learn an optimal ranking policy which maximizes
the expected accumulative rewards in a search session. Firstly, we formally
define the concept of search session Markov decision process (SSMDP) to
formulate the multi-step ranking problem. Secondly, we analyze the property of
SSMDP and theoretically prove the necessity of maximizing accumulative rewards.
Lastly, we propose a novel policy gradient algorithm for learning an optimal
ranking policy, which is able to deal with the problem of high reward variance
and unbalanced reward distribution of an SSMDP. Experiments are conducted in
simulation and TaoBao search engine. The results demonstrate that our algorithm
performs much better than online LTR methods, with more than 40% and 30% growth
of total transaction amount in the simulation and the real application,
respectively.
|
For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.
|
http://arxiv.org/abs/1803.00710v3
|
http://arxiv.org/pdf/1803.00710v3.pdf
| null |
[
"Yujing Hu",
"Qing Da",
"An-Xiang Zeng",
"Yang Yu",
"Yinghui Xu"
] |
[
"Decision Making",
"Learning-To-Rank",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-03-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/grounding-the-semantics-of-part-of-day-nouns
|
1805.09055
| null | null |
Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter
|
The usage of part-of-day nouns, such as 'night', and their time-specific
greetings ('good night'), varies across languages and cultures. We show the
possibilities that Twitter offers for studying the semantics of these terms and
its variability between countries. We mine a worldwide sample of multilingual
tweets with temporal greetings, and study how their frequencies vary in
relation with local time. The results provide insights into the semantics of
these temporal expressions and the cultural and sociological factors
influencing their usage.
|
The usage of part-of-day nouns, such as 'night', and their time-specific greetings ('good night'), varies across languages and cultures.
|
http://arxiv.org/abs/1805.09055v1
|
http://arxiv.org/pdf/1805.09055v1.pdf
|
WS 2018 6
|
[
"David Vilares",
"Carlos Gómez-Rodríguez"
] |
[] | 2018-05-23T00:00:00 |
https://aclanthology.org/W18-1116
|
https://aclanthology.org/W18-1116.pdf
|
grounding-the-semantics-of-part-of-day-nouns-1
| null |
[] |
https://paperswithcode.com/paper/deep-learning-generalizes-because-the
|
1805.08522
| null |
rye4g3AqFm
|
Deep learning generalizes because the parameter-function map is biased towards simple functions
|
Deep neural networks (DNNs) generalize remarkably well without explicit
regularization even in the strongly over-parametrized regime where classical
learning theory would instead predict that they would severely overfit. While
many proposals for some kind of implicit regularization have been made to
rationalise this success, there is no consensus for the fundamental reason why
DNNs do not strongly overfit. In this paper, we provide a new explanation. By
applying a very general probability-complexity bound recently derived from
algorithmic information theory (AIT), we argue that the parameter-function map
of many DNNs should be exponentially biased towards simple functions. We then
provide clear evidence for this strong simplicity bias in a model DNN for
Boolean functions, as well as in much larger fully connected and convolutional
networks applied to CIFAR10 and MNIST. As the target functions in many real
problems are expected to be highly structured, this intrinsic simplicity bias
helps explain why deep networks generalize well on real world problems. This
picture also facilitates a novel PAC-Bayes approach where the prior is taken
over the DNN input-output function space, rather than the more conventional
prior over parameter space. If we assume that the training algorithm samples
parameters close to uniformly within the zero-error region then the PAC-Bayes
theorem can be used to guarantee good expected generalization for target
functions producing high-likelihood training sets. By exploiting recently
discovered connections between DNNs and Gaussian processes to estimate the
marginal likelihood, we produce relatively tight generalization PAC-Bayes error
bounds which correlate well with the true error on realistic datasets such as
MNIST and CIFAR10 and for architectures including convolutional and fully
connected networks.
| null |
http://arxiv.org/abs/1805.08522v5
|
http://arxiv.org/pdf/1805.08522v5.pdf
|
ICLR 2019 5
|
[
"Guillermo Valle-Pérez",
"Chico Q. Camargo",
"Ard A. Louis"
] |
[
"Gaussian Processes",
"Learning Theory"
] | 2018-05-22T00:00:00 |
https://openreview.net/forum?id=rye4g3AqFm
|
https://openreview.net/pdf?id=rye4g3AqFm
|
deep-learning-generalizes-because-the-1
| null |
[] |
https://paperswithcode.com/paper/representation-balancing-mdps-for-off-policy
|
1805.09044
| null | null |
Representation Balancing MDPs for Off-Policy Policy Evaluation
|
We study the problem of off-policy policy evaluation (OPPE) in RL. In
contrast to prior work, we consider how to estimate both the individual policy
value and average policy value accurately. We draw inspiration from recent work
in causal reasoning, and propose a new finite sample generalization error bound
for value estimates from MDP models. Using this upper bound as an objective, we
develop a learning algorithm of an MDP model with a balanced representation,
and show that our approach can yield substantially lower MSE in common
synthetic benchmarks and a HIV treatment simulation domain.
|
We study the problem of off-policy policy evaluation (OPPE) in RL.
|
http://arxiv.org/abs/1805.09044v4
|
http://arxiv.org/pdf/1805.09044v4.pdf
|
NeurIPS 2018 12
|
[
"Yao Liu",
"Omer Gottesman",
"Aniruddh Raghu",
"Matthieu Komorowski",
"Aldo Faisal",
"Finale Doshi-Velez",
"Emma Brunskill"
] |
[] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7530-representation-balancing-mdps-for-off-policy-policy-evaluation
|
http://papers.nips.cc/paper/7530-representation-balancing-mdps-for-off-policy-policy-evaluation.pdf
|
representation-balancing-mdps-for-off-policy-1
| null |
[] |
https://paperswithcode.com/paper/generalisation-of-structural-knowledge-in-the
|
1805.09042
| null | null |
Generalisation of structural knowledge in the hippocampal-entorhinal system
|
A central problem to understanding intelligence is the concept of
generalisation. This allows previously learnt structure to be exploited to
solve tasks in novel situations differing in their particularities. We take
inspiration from neuroscience, specifically the hippocampal-entorhinal system
known to be important for generalisation. We propose that to generalise
structural knowledge, the representations of the structure of the world, i.e.
how entities in the world relate to each other, need to be separated from
representations of the entities themselves. We show, under these principles,
artificial neural networks embedded with hierarchy and fast Hebbian memory, can
learn the statistics of memories and generalise structural knowledge. Spatial
neuronal representations mirroring those found in the brain emerge, suggesting
spatial cognition is an instance of more general organising principles. We
further unify many entorhinal cell types as basis functions for constructing
transition graphs, and show these representations effectively utilise memories.
We experimentally support model assumptions, showing a preserved relationship
between entorhinal grid and hippocampal place cells across environments.
| null |
http://arxiv.org/abs/1805.09042v2
|
http://arxiv.org/pdf/1805.09042v2.pdf
|
NeurIPS 2018 12
|
[
"James C. R. Whittington",
"Timothy H. Muller",
"Shirley Mark",
"Caswell Barry",
"Timothy E. J. Behrens"
] |
[] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/8068-generalisation-of-structural-knowledge-in-the-hippocampal-entorhinal-system
|
http://papers.nips.cc/paper/8068-generalisation-of-structural-knowledge-in-the-hippocampal-entorhinal-system.pdf
|
generalisation-of-structural-knowledge-in-the-1
| null |
[] |
https://paperswithcode.com/paper/learning-how-to-be-robust-deep-polynomial
|
1804.06504
| null | null |
Learning how to be robust: Deep polynomial regression
|
Polynomial regression is a recurrent problem with a large number of
applications. In computer vision it often appears in motion analysis. Whatever
the application, standard methods for regression of polynomial models tend to
deliver biased results when the input data is heavily contaminated by outliers.
Moreover, the problem is even harder when outliers have strong structure.
Departing from problem-tailored heuristics for robust estimation of parametric
models, we explore deep convolutional neural networks. Our work aims to find a
generic approach for training deep regression models without the explicit need
of supervised annotation. We bypass the need for a tailored loss function on
the regression parameters by attaching to our model a differentiable hard-wired
decoder corresponding to the polynomial operation at hand. We demonstrate the
value of our findings by comparing with standard robust regression methods.
Furthermore, we demonstrate how to use such models for a real computer vision
problem, i.e., video stabilization. The qualitative and quantitative
experiments show that neural networks are able to learn robustness for general
polynomial regression, with results that well overpass scores of traditional
robust estimation methods.
| null |
http://arxiv.org/abs/1804.06504v2
|
http://arxiv.org/pdf/1804.06504v2.pdf
| null |
[
"Juan-Manuel Perez-Rua",
"Tomas Crivelli",
"Patrick Bouthemy",
"Patrick Perez"
] |
[
"Decoder",
"regression",
"Video Stabilization"
] | 2018-04-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/saliency-deep-embedding-for-aurora-image
|
1805.09033
| null | null |
Saliency deep embedding for aurora image search
|
Deep neural networks have achieved remarkable success in the field of image
search. However, the state-of-the-art algorithms are trained and tested for
natural images captured with ordinary cameras. In this paper, we aim to explore
a new search method for images captured with circular fisheye lens, especially
the aurora images. To reduce the interference from uninformative regions and
focus on the most interested regions, we propose a saliency proposal network
(SPN) to replace the region proposal network (RPN) in the recent Mask R-CNN. In
our SPN, the centers of the anchors are not distributed in a rectangular
meshing manner, but exhibit spherical distortion. Additionally, the directions
of the anchors are along the deformation lines perpendicular to the magnetic
meridian, which perfectly accords with the imaging principle of circular
fisheye lens. Extensive experiments are performed on the big aurora data,
demonstrating the superiority of our method in both search accuracy and
efficiency.
| null |
http://arxiv.org/abs/1805.09033v1
|
http://arxiv.org/pdf/1805.09033v1.pdf
| null |
[
"Xi Yang",
"Xinbo Gao",
"Bin Song",
"Nannan Wang",
"Dong Yang"
] |
[
"Image Retrieval",
"Region Proposal"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.\r\n\r\nRPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.",
"full_name": "Region Proposal Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Region Proposal",
"parent": null
},
"name": "RPN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/facebookresearch/detectron2/blob/bb9f5d8e613358519c9865609ab3fe7b6571f2ba/detectron2/layers/roi_align.py#L51",
"description": "**Region of Interest Align**, or **RoIAlign**, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh quantization of [RoI Pool](https://paperswithcode.com/method/roi-pooling), properly *aligning* the extracted features with the input. To avoid any quantization of the RoI boundaries or bins (using $x/16$ instead of $[x/16]$), RoIAlign uses bilinear interpolation to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and the result is then aggregated (using max or average).",
"full_name": "RoIAlign",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "RoIAlign",
"source_title": "Mask R-CNN",
"source_url": "http://arxiv.org/abs/1703.06870v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/facebookresearch/detectron2/blob/601d7666faaf7eb0ba64c9f9ce5811b13861fe12/detectron2/modeling/roi_heads/mask_head.py#L154",
"description": "**Mask R-CNN** extends [Faster R-CNN](http://paperswithcode.com/method/faster-r-cnn) to solve instance segmentation tasks. It achieves this by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. In principle, Mask R-CNN is an intuitive extension of Faster [R-CNN](https://paperswithcode.com/method/r-cnn), but constructing the mask branch properly is critical for good results. \r\n\r\nMost importantly, Faster R-CNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is evident in how [RoIPool](http://paperswithcode.com/method/roi-pooling), the *de facto* core operation for attending to instances, performs coarse spatial quantization for feature extraction. To fix the misalignment, Mask R-CNN utilises a simple, quantization-free layer, called [RoIAlign](http://paperswithcode.com/method/roi-align), that faithfully preserves exact spatial locations. \r\n\r\nSecondly, Mask R-CNN *decouples* mask and class prediction: it predicts a binary mask for each class independently, without competition among classes, and relies on the network's RoI classification branch to predict the category. In contrast, an [FCN](http://paperswithcode.com/method/fcn) usually perform per-pixel multi-class categorization, which couples segmentation and classification.",
"full_name": "Mask R-CNN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Instance Segmentation** models are models that perform the task of [Instance Segmentation](https://paperswithcode.com/task/instance-segmentation).",
"name": "Instance Segmentation Models",
"parent": null
},
"name": "Mask R-CNN",
"source_title": "Mask R-CNN",
"source_url": "http://arxiv.org/abs/1703.06870v3"
}
] |
https://paperswithcode.com/paper/empirical-analysis-of-foundational
|
1803.09840
| null | null |
Empirical Analysis of Foundational Distinctions in Linked Open Data
|
The Web and its Semantic extension (i.e. Linked Open Data) contain open
global-scale knowledge and make it available to potentially intelligent
machines that want to benefit from it. Nevertheless, most of Linked Open Data
lack ontological distinctions and have sparse axiomatisation. For example,
distinctions such as whether an entity is inherently a class or an individual,
or whether it is a physical object or not, are hardly expressed in the data,
although they have been largely studied and formalised by foundational
ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too,
which is relevant for many artificial intelligence tasks such as natural
language understanding, scene recognition, and the like. There is a gap between
foundational ontologies, that often formalise or are inspired by pre-existing
philosophical theories and are developed with a top-down approach, and Linked
Open Data that mostly derive from existing databases or crowd-based effort
(e.g. DBpedia, Wikidata). We investigate whether machines can learn
foundational distinctions over Linked Open Data entities, and if they match
common sense. We want to answer questions such as "does the DBpedia entity for
dog refer to a class or to an instance?". We report on a set of experiments
based on machine learning and crowdsourcing that show promising results.
|
For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e. g. DOLCE, SUMO).
|
http://arxiv.org/abs/1803.09840v2
|
http://arxiv.org/pdf/1803.09840v2.pdf
| null |
[
"Luigi Asprino",
"Valerio Basile",
"Paolo Ciancarini",
"Valentina Presutti"
] |
[
"Common Sense Reasoning",
"Natural Language Understanding",
"Scene Recognition"
] | 2018-03-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/minimum-margin-loss-for-deep-face-recognition
|
1805.06741
| null | null |
Minimum Margin Loss for Deep Face Recognition
|
Face recognition has achieved great progress owing to the fast development of
the deep neural network in the past a few years. As an important part of deep
neural networks, a number of the loss functions have been proposed which
significantly improve the state-of-the-art methods. In this paper, we proposed
a new loss function called Minimum Margin Loss (MML) which aims at enlarging
the margin of those overclose class centre pairs so as to enhance the
discriminative ability of the deep features. MML supervises the training
process together with the Softmax Loss and the Centre Loss, and also makes up
the defect of Softmax + Centre Loss. The experimental results on MegaFace, LFW
and YTF datasets show that the proposed method achieves the state-of-the-art
performance, which demonstrates the effectiveness of the proposed MML.
| null |
http://arxiv.org/abs/1805.06741v4
|
http://arxiv.org/pdf/1805.06741v4.pdf
| null |
[
"Xin Wei",
"Hui Wang",
"Bryan Scotney",
"Huan Wan"
] |
[
"Face Recognition"
] | 2018-05-17T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/efficient-relaxations-for-dense-crfs-with
|
1805.09028
| null | null |
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
|
Dense conditional random fields (CRFs) have become a popular framework for
modelling several problems in computer vision such as stereo correspondence and
multi-class semantic segmentation. By modelling long-range interactions, dense
CRFs provide a labelling that captures finer detail than their sparse
counterparts. Currently, the state-of-the-art algorithm performs mean-field
inference using a filter-based method but fails to provide a strong theoretical
guarantee on the quality of the solution. A question naturally arises as to
whether it is possible to obtain a maximum a posteriori (MAP) estimate of a
dense CRF using a principled method. Within this paper, we show that this is
indeed possible. We will show that, by using a filter-based method, continuous
relaxations of the MAP problem can be optimised efficiently using
state-of-the-art algorithms. Specifically, we will solve a quadratic
programming (QP) relaxation using the Frank-Wolfe algorithm and a linear
programming (LP) relaxation by developing a proximal minimisation framework. By
exploiting labelling consistency in the higher-order potentials and utilising
the filter-based method, we are able to formulate the above algorithms such
that each iteration has a complexity linear in the number of classes and random
variables. The presented algorithms can be applied to any labelling problem
using a dense CRF with sparse higher-order potentials. In this paper, we use
semantic segmentation as an example application as it demonstrates the ability
of the algorithm to scale to dense CRFs with large dimensions. We perform
experiments on the Pascal dataset to indicate that the presented algorithms are
able to attain lower energies than the mean-field inference method.
| null |
http://arxiv.org/abs/1805.09028v2
|
http://arxiv.org/pdf/1805.09028v2.pdf
| null |
[
"Thomas Joy",
"Alban Desmaison",
"Thalaiyasingam Ajanthan",
"Rudy Bunel",
"Mathieu Salzmann",
"Pushmeet Kohli",
"Philip H. S. Torr",
"M. Pawan Kumar"
] |
[
"Semantic Segmentation"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linear chain CRFs are popular in natural language processing, whereas in image-based tasks, the graph would connect to neighboring locations in an image to enforce that they have similar predictions.\r\n\r\nImage Credit: [Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields](https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf)",
"full_name": "Conditional Random Field",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Structured Prediction** methods deal with structured outputs with multiple interdependent outputs. Below you can find a continuously updating list of structured prediction methods.",
"name": "Structured Prediction",
"parent": null
},
"name": "CRF",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/data-driven-forecasting-of-high-dimensional
|
1802.07486
| null | null |
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
|
We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.
| null |
https://arxiv.org/abs/1802.07486v5
|
https://arxiv.org/pdf/1802.07486v5.pdf
| null |
[
"Pantelis R. Vlachas",
"Wonmin Byeon",
"Zhong Y. Wan",
"Themistoklis P. Sapsis",
"Petros Koumoutsakos"
] |
[
"Gaussian Processes",
"Time Series",
"Time Series Analysis"
] | 2018-02-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/phocas-dimensional-byzantine-resilient
|
1805.09682
| null | null |
Phocas: dimensional Byzantine-resilient stochastic gradient descent
|
We propose a novel robust aggregation rule for distributed synchronous
Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The
attackers can arbitrarily manipulate the data transferred between the servers
and the workers in the parameter server~(PS) architecture. We prove the
Byzantine resilience of the proposed aggregation rules. Empirical analysis
shows that the proposed techniques outperform current approaches for realistic
use cases and Byzantine attack scenarios.
| null |
http://arxiv.org/abs/1805.09682v1
|
http://arxiv.org/pdf/1805.09682v1.pdf
| null |
[
"Cong Xie",
"Oluwasanmi Koyejo",
"Indranil Gupta"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/universal-language-model-fine-tuning-for-text
|
1801.06146
| null | null |
Universal Language Model Fine-tuning for Text Classification
|
Inductive transfer learning has greatly impacted computer vision, but
existing approaches in NLP still require task-specific modifications and
training from scratch. We propose Universal Language Model Fine-tuning
(ULMFiT), an effective transfer learning method that can be applied to any task
in NLP, and introduce techniques that are key for fine-tuning a language model.
Our method significantly outperforms the state-of-the-art on six text
classification tasks, reducing the error by 18-24% on the majority of datasets.
Furthermore, with only 100 labeled examples, it matches the performance of
training from scratch on 100x more data. We open-source our pretrained models
and code.
|
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.
|
http://arxiv.org/abs/1801.06146v5
|
http://arxiv.org/pdf/1801.06146v5.pdf
|
ACL 2018 7
|
[
"Jeremy Howard",
"Sebastian Ruder"
] |
[
"General Classification",
"Language Modeling",
"Language Modelling",
"Sentiment Analysis",
"Text Classification",
"Transfer Learning"
] | 2018-01-18T00:00:00 |
https://aclanthology.org/P18-1031
|
https://aclanthology.org/P18-1031.pdf
|
universal-language-model-fine-tuning-for-text-1
| null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/salesforce/awd-lstm-lm/blob/32fcb42562aeb5c7e6c9dec3f2a3baaaf68a5cb5/main.py#L202",
"description": "**Temporal Activation Regularization (TAR)** is a type of slowness regularization for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) that penalizes differences between states that have been explored in the past. Formally we minimize:\r\n\r\n$$\\beta{L\\_{2}}\\left(h\\_{t} - h\\_{t+1}\\right)$$\r\n\r\nwhere $L\\_{2}$ is the $L\\_{2}$ norm, $h_{t}$ is the output of the RNN at timestep $t$, and $\\beta$ is a scaling coefficient.",
"full_name": "Temporal Activation Regularization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Temporal Activation Regularization",
"source_title": "Revisiting Activation Regularization for Language RNNs",
"source_url": "http://arxiv.org/abs/1708.01009v1"
},
{
"code_snippet_url": "https://github.com/teelinsan/KerasDropconnect/blob/0b203b56b2564686358fe0907b89316e3fce4014/ddrop/layers.py#L24",
"description": "**DropConnect** generalizes [Dropout](https://paperswithcode.com/method/dropout) by randomly dropping the weights rather than the activations with probability $1-p$. DropConnect is similar to Dropout as it introduces dynamic sparsity within the model, but differs in that the sparsity is on the weights $W$, rather than the output vectors of a layer. In other words, the fully connected layer with DropConnect becomes a sparsely connected layer in which the connections are chosen at random during the training stage. Note that this is not equivalent to setting $W$ to be a fixed sparse matrix during training.\r\n\r\nFor a DropConnect layer, the output is given as:\r\n\r\n$$ r = a \\left(\\left(M * W\\right){v}\\right)$$\r\n\r\nHere $r$ is the output of a layer, $v$ is the input to a layer, $W$ are weight parameters, and $M$ is a binary matrix encoding the connection information where $M\\_{ij} \\sim \\text{Bernoulli}\\left(p\\right)$. Each element of the mask $M$ is drawn independently for each example during training, essentially instantiating a different connectivity for each example seen. Additionally, the biases are also masked out during training.",
"full_name": "DropConnect",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "DropConnect",
"source_title": "Regularization of Neural Networks using DropConnect",
"source_url": "http://cds.nyu.edu/projects/regularization-neural-networks-using-dropconnect/"
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/salesforce/awd-lstm-lm/blob/32fcb42562aeb5c7e6c9dec3f2a3baaaf68a5cb5/main.py#L200",
"description": "**Activation Regularization (AR)**, or $L\\_{2}$ activation regularization, is regularization performed on activations as opposed to weights. It is usually used in conjunction with [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks). It is defined as:\r\n\r\n$$\\alpha{L}\\_{2}\\left(m\\circ{h\\_{t}}\\right) $$\r\n\r\nwhere $m$ is a [dropout](https://paperswithcode.com/method/dropout) mask used by later parts of the model, $L\\_{2}$ is the $L\\_{2}$ norm, and $h_{t}$ is the output of an RNN at timestep $t$, and $\\alpha$ is a scaling coefficient. \r\n\r\nWhen applied to the output of a dense layer, AR penalizes activations that are substantially away from 0, encouraging activations to remain small.",
"full_name": "Activation Regularization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Activation Regularization",
"source_title": "Revisiting Activation Regularization for Language RNNs",
"source_url": "http://arxiv.org/abs/1708.01009v1"
},
{
"code_snippet_url": "https://github.com/salesforce/awd-lstm-lm/blob/32fcb42562aeb5c7e6c9dec3f2a3baaaf68a5cb5/embed_regularize.py#L5",
"description": "**Embedding Dropout** is equivalent to performing [dropout](https://paperswithcode.com/method/dropout) on the embedding matrix at a word level, where the dropout is broadcast across all the word vector’s embedding. The remaining non-dropped-out word embeddings are scaled by $\\frac{1}{1-p\\_{e}}$ where $p\\_{e}$ is the probability of embedding dropout. As the dropout occurs on the embedding matrix that is used for a full forward and backward pass, this means that all occurrences of a specific word will disappear within that pass, equivalent to performing [variational dropout](https://paperswithcode.com/method/variational-dropout) on the connection between the one-hot embedding and the embedding lookup.\r\n\r\nSource: Merity et al, Regularizing and Optimizing [LSTM](https://paperswithcode.com/method/lstm) Language Models",
"full_name": "Embedding Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Embedding Dropout",
"source_title": "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks",
"source_url": "http://arxiv.org/abs/1512.05287v5"
},
{
"code_snippet_url": "https://github.com/salesforce/awd-lstm-lm/blob/32fcb42562aeb5c7e6c9dec3f2a3baaaf68a5cb5/weight_drop.py#L5",
"description": "**Variational Dropout** is a regularization technique based on [dropout](https://paperswithcode.com/method/dropout), but uses a variational inference grounded approach. In Variational Dropout, we repeat the same dropout mask at each time step for both inputs, outputs, and recurrent layers (drop the same network units at each time step). This is in contrast to ordinary Dropout where different dropout masks are sampled at each time step for the inputs and outputs alone.",
"full_name": "Variational Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Variational Dropout",
"source_title": "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks",
"source_url": "http://arxiv.org/abs/1512.05287v5"
},
{
"code_snippet_url": "",
"description": "**Weight Tying** improves the performance of language models by tying (sharing) the weights of the embedding and [softmax](https://paperswithcode.com/method/softmax) layers. This method also massively reduces the total number of parameters in the language models that it is applied to. \r\n\r\nLanguage models are typically comprised of an embedding layer, followed by a number of [Transformer](https://paperswithcode.com/method/transformer) or [LSTM](https://paperswithcode.com/method/lstm) layers, which are finally followed by a softmax layer. Embedding layers learn word representations, such that similar words (in meaning) are represented by vectors that are near each other (in cosine distance). [Press & Wolf, 2016] showed that the softmax matrix, in which every word also has a vector representation, also exhibits this property. This leads them to propose to share the softmax and embedding matrices, which is done today in nearly all language models. \r\n\r\nThis method was independently introduced by [Press & Wolf, 2016](https://paperswithcode.com/paper/using-the-output-embedding-to-improve) and [Inan et al, 2016](https://paperswithcode.com/paper/tying-word-vectors-and-word-classifiers-a).\r\n\r\nAdditionally, the Press & Wolf paper proposes Three-way Weight Tying, a method for NMT models in which the embedding matrix for the source language, the embedding matrix for the target language, and the softmax matrix for the target language are all tied. That method has been adopted by the Attention Is All You Need model and many other neural machine translation models.",
"full_name": "Weight Tying",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Parameter Sharing** methods are used in neural networks to control the overall number of parameters and help guard against overfitting. Below you can find a continuously updating list of parameter sharing methods.",
"name": "Parameter Sharing",
"parent": null
},
"name": "Weight Tying",
"source_title": "Using the Output Embedding to Improve Language Models",
"source_url": "http://arxiv.org/abs/1608.05859v3"
},
{
"code_snippet_url": null,
"description": "**ASGD Weight-Dropped LSTM**, or **AWD-LSTM**, is a type of recurrent neural network that employs [DropConnect](https://paperswithcode.com/method/dropconnect) for regularization, as well as [NT-ASGD](https://paperswithcode.com/method/nt-asgd) for optimization - non-monotonically triggered averaged [SGD](https://paperswithcode.com/method/sgd) - which returns an average of last iterations of weights. Additional regularization techniques employed include variable length backpropagation sequences, [variational dropout](https://paperswithcode.com/method/variational-dropout), [embedding dropout](https://paperswithcode.com/method/embedding-dropout), [weight tying](https://paperswithcode.com/method/weight-tying), independent embedding/hidden size, [activation regularization](https://paperswithcode.com/method/activation-regularization) and [temporal activation regularization](https://paperswithcode.com/method/temporal-activation-regularization).",
"full_name": "ASGD Weight-Dropped LSTM",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "AWD-LSTM",
"source_title": "Regularizing and Optimizing LSTM Language Models",
"source_url": "http://arxiv.org/abs/1708.02182v1"
},
{
"code_snippet_url": "https://github.com/fastai/fastai/blob/43001e17ba469308e9688dfe99a891018bcf7ad4/courses/dl2/imdb_scripts/finetune_lm.py#L132",
"description": "**Discriminative Fine-Tuning** is a fine-tuning strategy that is used for [ULMFiT](https://paperswithcode.com/method/ulmfit) type models. Instead of using the same learning rate for all layers of the model, discriminative fine-tuning allows us to tune each layer with different learning rates. For context, the regular stochastic gradient descent ([SGD](https://paperswithcode.com/method/sgd)) update of a model’s parameters $\\theta$ at time step $t$ looks like the following (Ruder, 2016):\r\n\r\n$$ \\theta\\_{t} = \\theta\\_{t-1} − \\eta\\cdot\\nabla\\_{\\theta}J\\left(\\theta\\right)$$\r\n\r\nwhere $\\eta$ is the learning rate and $\\nabla\\_{\\theta}J\\left(\\theta\\right)$ is the gradient with regard to the model’s objective function. For discriminative fine-tuning, we split the parameters $\\theta$ into {$\\theta\\_{1}, \\ldots, \\theta\\_{L}$} where $\\theta\\_{l}$ contains the parameters of the model at the $l$-th layer and $L$ is the number of layers of the model. Similarly, we obtain {$\\eta\\_{1}, \\ldots, \\eta\\_{L}$} where $\\theta\\_{l}$ where $\\eta\\_{l}$ is the learning rate of the $l$-th layer. The SGD update with discriminative finetuning is then:\r\n\r\n$$ \\theta\\_{t}^{l} = \\theta\\_{t-1}^{l} - \\eta^{l}\\cdot\\nabla\\_{\\theta^{l}}J\\left(\\theta\\right) $$\r\n\r\nThe authors find that empirically it worked well to first choose the learning rate $\\eta^{L}$ of the last layer by fine-tuning only the last layer and using $\\eta^{l-1}=\\eta^{l}/2.6$ as the learning rate for lower layers.",
"full_name": "Discriminative Fine-Tuning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Fine-Tuning** methods in deep learning take existing trained networks and 'fine-tune' them to a new task so that information contained in the weights can be repurposed. Below you can find a continuously updating list of fine-tuning methods.",
"name": "Fine-Tuning",
"parent": null
},
"name": "Discriminative Fine-Tuning",
"source_title": "Universal Language Model Fine-tuning for Text Classification",
"source_url": "http://arxiv.org/abs/1801.06146v5"
},
{
"code_snippet_url": null,
"description": "**Slanted Triangular Learning Rates (STLR)** is a learning rate schedule which first linearly increases the learning rate and then linearly decays it, which can be seen in Figure to the right. It is a modification of Triangular Learning Rates, with a short increase and a long decay period.",
"full_name": "Slanted Triangular Learning Rates",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Slanted Triangular Learning Rates",
"source_title": "Universal Language Model Fine-tuning for Text Classification",
"source_url": "http://arxiv.org/abs/1801.06146v5"
},
{
"code_snippet_url": "",
"description": "**Universal Language Model Fine-tuning**, or **ULMFiT**, is an architecture and transfer learning method that can be applied to NLP tasks. It involves a 3-layer [AWD-LSTM](https://paperswithcode.com/method/awd-lstm) architecture for its representations. The training consists of three steps: 1) general language model pre-training on a Wikipedia-based text, 2) fine-tuning the language model on a target task, and 3) fine-tuning the classifier on the target task.\r\n\r\nAs different layers capture different types of information, they are fine-tuned to different extents using [discriminative fine-tuning](https://paperswithcode.com/method/discriminative-fine-tuning). Training is performed using [Slanted triangular learning rates](https://paperswithcode.com/method/slanted-triangular-learning-rates) (STLR), a learning rate scheduling strategy that first linearly increases the learning rate and then linearly decays it.\r\n\r\nFine-tuning the target classifier is achieved in ULMFiT using gradual unfreezing. Rather than fine-tuning all layers at once, which risks catastrophic forgetting, ULMFiT gradually unfreezes the model starting from the last layer (i.e., closest to the output) as this contains the least general knowledge. First the last layer is unfrozen and all unfrozen layers are fine-tuned for one epoch. Then the next group of frozen layers is unfrozen and fine-tuned and repeat, until all layers are fine-tuned until convergence at the last iteration.",
"full_name": "Universal Language Model Fine-tuning",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "**Language Models** are models for predicting the next word or character in a document. Below you can find a continuously updating list of language models.\r\n\r\n",
"name": "Language Models",
"parent": null
},
"name": "ULMFiT",
"source_title": "Universal Language Model Fine-tuning for Text Classification",
"source_url": "http://arxiv.org/abs/1801.06146v5"
}
] |
https://paperswithcode.com/paper/addressing-the-item-cold-start-problem-by
|
1805.09023
| null | null |
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
|
In recommender systems, cold-start issues are situations where no previous
events, e.g. ratings, are known for certain users or items. In this paper, we
focus on the item cold-start problem. Both content information (e.g. item
attributes) and initial user ratings are valuable for seizing users'
preferences on a new item. However, previous methods for the item cold-start
problem either 1) incorporate content information into collaborative filtering
to perform hybrid recommendation, or 2) actively select users to rate the new
item without considering content information and then do collaborative
filtering. In this paper, we propose a novel recommendation scheme for the item
cold-start problem by leverage both active learning and items' attribute
information. Specifically, we design useful user selection criteria based on
items' attributes and users' rating history, and combine the criteria in an
optimization framework for selecting users. By exploiting the feedback ratings,
users' previous ratings and items' attributes, we then generate accurate rating
predictions for the other unselected users. Experimental results on two
real-world datasets show the superiority of our proposed method over
traditional methods.
| null |
http://arxiv.org/abs/1805.09023v1
|
http://arxiv.org/pdf/1805.09023v1.pdf
| null |
[
"Yu Zhu",
"Jinhao Lin",
"Shibi He",
"Beidou Wang",
"Ziyu Guan",
"Haifeng Liu",
"Deng Cai"
] |
[
"Active Learning",
"Attribute",
"Collaborative Filtering",
"Recommendation Systems"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cnncnn-convolutional-decoders-for-image
|
1805.09019
| null | null |
CNN+CNN: Convolutional Decoders for Image Captioning
|
Image captioning is a challenging task that combines the field of computer
vision and natural language processing. A variety of approaches have been
proposed to achieve the goal of automatically describing an image, and
recurrent neural network (RNN) or long-short term memory (LSTM) based models
dominate this field. However, RNNs or LSTMs cannot be calculated in parallel
and ignore the underlying hierarchical structure of a sentence. In this paper,
we propose a framework that only employs convolutional neural networks (CNNs)
to generate captions. Owing to parallel computing, our basic model is around 3
times faster than NIC (an LSTM-based model) during training time, while also
providing better results. We conduct extensive experiments on MSCOCO and
investigate the influence of the model width and depth. Compared with
LSTM-based models that apply similar attention mechanisms, our proposed models
achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of
CIDEr. We also test our model on the paragraph annotation dataset, and get
higher CIDEr score compared with hierarchical LSTMs
|
We also test our model on the paragraph annotation dataset, and get higher CIDEr score compared with hierarchical LSTMs
|
http://arxiv.org/abs/1805.09019v1
|
http://arxiv.org/pdf/1805.09019v1.pdf
| null |
[
"Qingzhong Wang",
"Antoni B. Chan"
] |
[
"Image Captioning",
"Sentence"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-attentional-communication-for-multi
|
1805.07733
| null | null |
Learning Attentional Communication for Multi-Agent Cooperation
|
Communication could potentially be an effective way for multi-agent
cooperation. However, information sharing among all agents or in predefined
communication architectures that existing methods adopt can be problematic.
When there is a large number of agents, agents cannot differentiate valuable
information that helps cooperative decision making from globally shared
information. Therefore, communication barely helps, and could even impair the
learning of multi-agent cooperation. Predefined communication architectures, on
the other hand, restrict communication among agents and thus restrain potential
cooperation. To tackle these difficulties, in this paper, we propose an
attentional communication model that learns when communication is needed and
how to integrate shared information for cooperative decision making. Our model
leads to efficient and effective communication for large-scale multi-agent
cooperation. Empirically, we show the strength of our model in a variety of
cooperative scenarios, where agents are able to develop more coordinated and
sophisticated strategies than existing methods.
| null |
http://arxiv.org/abs/1805.07733v3
|
http://arxiv.org/pdf/1805.07733v3.pdf
|
NeurIPS 2018 12
|
[
"Jiechuan Jiang",
"Zongqing Lu"
] |
[
"Decision Making"
] | 2018-05-20T00:00:00 |
http://papers.nips.cc/paper/7956-learning-attentional-communication-for-multi-agent-cooperation
|
http://papers.nips.cc/paper/7956-learning-attentional-communication-for-multi-agent-cooperation.pdf
|
learning-attentional-communication-for-multi-1
| null |
[] |
https://paperswithcode.com/paper/transfer-learning-for-illustration
|
1806.02682
| null | null |
Transfer Learning for Illustration Classification
|
The field of image classification has shown an outstanding success thanks to
the development of deep learning techniques. Despite the great performance
obtained, most of the work has focused on natural images ignoring other domains
like artistic depictions. In this paper, we use transfer learning techniques to
propose a new classification network with better performance in illustration
images. Starting from the deep convolutional network VGG19, pre-trained with
natural images, we propose two novel models which learn object representations
in the new domain. Our optimized network will learn new low-level features of
the images (colours, edges, textures) while keeping the knowledge of the
objects and shapes that it already learned from the ImageNet dataset. Thus,
requiring much less data for the training. We propose a novel dataset of
illustration images labelled by content where our optimized architecture
achieves $\textbf{86.61\%}$ of top-1 and $\textbf{97.21\%}$ of top-5 precision.
We additionally demonstrate that our model is still able to recognize objects
in photographs.
|
In this paper, we use transfer learning techniques to propose a new classification network with better performance in illustration images.
|
http://arxiv.org/abs/1806.02682v1
|
http://arxiv.org/pdf/1806.02682v1.pdf
| null |
[
"Manuel Lagunas",
"Elena Garces"
] |
[
"Classification",
"General Classification",
"image-classification",
"Image Classification",
"Transfer Learning"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/splinecnn-fast-geometric-deep-learning-with
|
1711.08920
| null | null |
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
|
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to the local support property of the B-spline basis functions. As a result,
we obtain a generalization of the traditional CNN convolution operator by using
continuous kernel functions parametrized by a fixed number of trainable
weights. In contrast to related approaches that filter in the spectral domain,
the proposed method aggregates features purely in the spatial domain. In
addition, SplineCNN allows entire end-to-end training of deep architectures,
using only the geometric structure as input, instead of handcrafted feature
descriptors. For validation, we apply our method on tasks from the fields of
image graph classification, shape correspondence and graph node classification,
and show that it outperforms or pars state-of-the-art approaches while being
significantly faster and having favorable properties like domain-independence.
|
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
|
http://arxiv.org/abs/1711.08920v2
|
http://arxiv.org/pdf/1711.08920v2.pdf
|
CVPR 2018 6
|
[
"Matthias Fey",
"Jan Eric Lenssen",
"Frank Weichert",
"Heinrich Müller"
] |
[
"Deep Learning",
"General Classification",
"Graph Classification",
"Node Classification",
"Superpixel Image Classification"
] | 2017-11-24T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Fey_SplineCNN_Fast_Geometric_CVPR_2018_paper.pdf
|
splinecnn-fast-geometric-deep-learning-with-1
| null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/bilingual-sentiment-embeddings-joint
|
1805.09016
| null | null |
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
|
Sentiment analysis in low-resource languages suffers from a lack of annotated
corpora to estimate high-performing models. Machine translation and bilingual
word embeddings provide some relief through cross-lingual sentiment approaches.
However, they either require large amounts of parallel data or do not
sufficiently capture sentiment information. We introduce Bilingual Sentiment
Embeddings (BLSE), which jointly represent sentiment information in a source
and target language. This model only requires a small bilingual lexicon, a
source-language corpus annotated for sentiment, and monolingual word embeddings
for each language. We perform experiments on three language combinations
(Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment
classification and find that our model significantly outperforms
state-of-the-art methods on four out of six experimental setups, as well as
capturing complementary information to machine translation. Our analysis of the
resulting embedding space provides evidence that it represents sentiment
information in the resource-poor target language without any annotated data in
that language.
|
Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models.
|
http://arxiv.org/abs/1805.09016v1
|
http://arxiv.org/pdf/1805.09016v1.pdf
|
ACL 2018 7
|
[
"Jeremy Barnes",
"Roman Klinger",
"Sabine Schulte im Walde"
] |
[
"Cross-Lingual Sentiment Classification",
"Machine Translation",
"Sentence",
"Sentiment Analysis",
"Sentiment Classification",
"Translation",
"Word Embeddings"
] | 2018-05-23T00:00:00 |
https://aclanthology.org/P18-1231
|
https://aclanthology.org/P18-1231.pdf
|
bilingual-sentiment-embeddings-joint-1
| null |
[] |
https://paperswithcode.com/paper/learning-to-design-games-strategic
|
1707.01310
| null | null |
Learning to Design Games: Strategic Environments in Reinforcement Learning
|
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment. In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time. This extension is motivated by environment design scenarios in the real-world, including game design, shopping space design and traffic signal design. Theoretically, we find a dual Markov decision process (MDP) w.r.t. the environment to that w.r.t. the agent, and derive a policy gradient solution to optimizing the parametrized environment. Furthermore, discontinuous environments are addressed by a proposed general generative framework. Our experiments on a Maze game design task show the effectiveness of the proposed algorithms in generating diverse and challenging Mazes against various agent settings.
| null |
https://arxiv.org/abs/1707.01310v5
|
https://arxiv.org/pdf/1707.01310v5.pdf
| null |
[
"Haifeng Zhang",
"Jun Wang",
"Zhiming Zhou",
"Wei-Nan Zhang",
"Ying Wen",
"Yong Yu",
"Wenxin Li"
] |
[
"Game Design",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2017-07-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/optimal-single-sample-tests-for-structured
|
1802.06186
| null | null |
Optimal Single Sample Tests for Structured versus Unstructured Network Data
|
We study the problem of testing, using only a single sample, between mean
field distributions (like Curie-Weiss, Erd\H{o}s-R\'enyi) and structured Gibbs
distributions (like Ising model on sparse graphs and Exponential Random
Graphs). Our goal is to test without knowing the parameter values of the
underlying models: only the \emph{structure} of dependencies is known. We
develop a new approach that applies to both the Ising and Exponential Random
Graph settings based on a general and natural statistical test. The test can
distinguish the hypotheses with high probability above a certain threshold in
the (inverse) temperature parameter, and is optimal in that below the threshold
no test can distinguish the hypotheses.
The thresholds do not correspond to the presence of long-range order in the
models. By aggregating information at a global scale, our test works even at
very high temperatures.
The proofs are based on distributional approximation and sharp concentration
of quadratic forms, when restricted to Hamming spheres. The restriction to
Hamming spheres is necessary, since otherwise any scalar statistic is useless
without explicit knowledge of the temperature parameter. At the same time, this
restriction radically changes the behavior of the functions under
consideration, resulting in a much smaller variance than in the independent
setting; this makes it hard to directly apply standard methods (i.e., Stein's
method) for concentration of weakly dependent variables. Instead, we carry out
an additional tensorization argument using a Markov chain that respects the
symmetry of the Hamming sphere.
| null |
http://arxiv.org/abs/1802.06186v2
|
http://arxiv.org/pdf/1802.06186v2.pdf
| null |
[
"Guy Bresler",
"Dheeraj Nagaraj"
] |
[] | 2018-02-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/wasserstein-discriminant-analysis
|
1608.08063
| null | null |
Wasserstein Discriminant Analysis
|
Wasserstein Discriminant Analysis (WDA) is a new supervised method that can
improve classification of high-dimensional data by computing a suitable linear
map onto a lower dimensional subspace. Following the blueprint of classical
Linear Discriminant Analysis (LDA), WDA selects the projection matrix that
maximizes the ratio of two quantities: the dispersion of projected points
coming from different classes, divided by the dispersion of projected points
coming from the same class. To quantify dispersion, WDA uses regularized
Wasserstein distances, rather than cross-variance measures which have been
usually considered, notably in LDA. Thanks to the the underlying principles of
optimal transport, WDA is able to capture both global (at distribution scale)
and local (at samples scale) interactions between classes. Regularized
Wasserstein distances can be computed using the Sinkhorn matrix scaling
algorithm; We show that the optimization of WDA can be tackled using automatic
differentiation of Sinkhorn iterations. Numerical experiments show promising
results both in terms of prediction and visualization on toy examples and real
life datasets such as MNIST and on deep features obtained from a subset of the
Caltech dataset.
|
Wasserstein Discriminant Analysis (WDA) is a new supervised method that can improve classification of high-dimensional data by computing a suitable linear map onto a lower dimensional subspace.
|
http://arxiv.org/abs/1608.08063v2
|
http://arxiv.org/pdf/1608.08063v2.pdf
| null |
[
"Rémi Flamary",
"Marco Cuturi",
"Nicolas Courty",
"Alain Rakotomamonjy"
] |
[] | 2016-08-29T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.\r\n\r\nExtracted from [Wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis)\r\n\r\n**Source**:\r\n\r\nPaper: [Linear Discriminant Analysis: A Detailed Tutorial](https://dx.doi.org/10.3233/AIC-170729)\r\n\r\nPublic version: [Linear Discriminant Analysis: A Detailed Tutorial](https://usir.salford.ac.uk/id/eprint/52074/)",
"full_name": "Linear Discriminant Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "LDA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/learning-sparse-structured-ensembles-with-sg
|
1803.00184
| null |
r1uOhfb0W
|
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning
|
An ensemble of neural networks is known to be more robust and accurate than
an individual network, however usually with linearly-increased cost in both
training and testing. In this work, we propose a two-stage method to learn
Sparse Structured Ensembles (SSEs) for neural networks. In the first stage, we
run SG-MCMC with group sparse priors to draw an ensemble of samples from the
posterior distribution of network parameters. In the second stage, we apply
weight-pruning to each sampled network and then perform retraining over the
remained connections. In this way of learning SSEs with SG-MCMC and pruning, we
not only achieve high prediction accuracy since SG-MCMC enhances exploration of
the model-parameter space, but also reduce memory and computation cost
significantly in both training and testing of NN ensembles. This is thoroughly
evaluated in the experiments of learning SSE ensembles of both FNNs and LSTMs.
For example, in LSTM based language modeling (LM), we obtain 21% relative
reduction in LM perplexity by learning a SSE of 4 large LSTM models, which has
only 30% of model parameters and 70% of computations in total, as compared to
the baseline large LSTM LM. To the best of our knowledge, this work represents
the first methodology and empirical study of integrating SG-MCMC, group sparse
prior and network pruning together for learning NN ensembles.
| null |
http://arxiv.org/abs/1803.00184v3
|
http://arxiv.org/pdf/1803.00184v3.pdf
|
ICLR 2018 1
|
[
"Yichi Zhang",
"Zhijian Ou"
] |
[
"Language Modeling",
"Language Modelling",
"Network Pruning"
] | 2018-03-01T00:00:00 |
https://openreview.net/forum?id=r1uOhfb0W
|
https://openreview.net/pdf?id=r1uOhfb0W
|
learning-sparse-structured-ensembles-with-sg-1
| null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Efficient ConvNets",
"source_url": "http://arxiv.org/abs/1608.08710v3"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/training-deep-learning-based-denoisers
|
1803.01314
| null | null |
Training Deep Learning Based Denoisers without Ground Truth Data
|
Recently developed deep-learning-based denoisers often outperform state-of-the-art conventional denoisers such as the BM3D. They are typically trained to minimize the mean squared error (MSE) between the output image of a deep neural network (DNN) and a ground truth image. Thus, it is important for deep-learning-based denoisers to use high quality noiseless ground truth data for high performance. However, it is often challenging or even infeasible to obtain noiseless images in some applications. Here, we propose a method based on Stein's unbiased risk estimator (SURE) for training DNN denoisers based only on the use of noisy images in the training data with Gaussian noise. We demonstrate that our SURE-based method, without the use of ground truth data, is able to train DNN denoisers to yield performances close to those networks trained with ground truth for both grayscale and color images. We also propose a SURE-based refining method with a noisy test image for further performance improvement. Our quick refining method outperformed conventional BM3D, deep image prior, and often the networks trained with ground truth. Potential extension of our SURE-based methods to Poisson noise model was also investigated.
|
Our quick refining method outperformed conventional BM3D, deep image prior, and often the networks trained with ground truth.
|
https://arxiv.org/abs/1803.01314v4
|
https://arxiv.org/pdf/1803.01314v4.pdf
|
NeurIPS 2018 12
|
[
"Shakarim Soltanayev",
"Se Young Chun"
] |
[
"Deep Learning",
"Image Denoising"
] | 2018-03-04T00:00:00 |
http://papers.nips.cc/paper/7587-training-deep-learning-based-denoisers-without-ground-truth-data
|
http://papers.nips.cc/paper/7587-training-deep-learning-based-denoisers-without-ground-truth-data.pdf
|
training-deep-learning-based-denoisers-1
| null |
[] |
https://paperswithcode.com/paper/a-transition-based-algorithm-for-unrestricted
|
1805.09007
| null | null |
A Transition-based Algorithm for Unrestricted AMR Parsing
|
Non-projective parsing can be useful to handle cycles and reentrancy in AMR
graphs. We explore this idea and introduce a greedy left-to-right
non-projective transition-based parser. At each parsing configuration, an
oracle decides whether to create a concept or whether to connect a pair of
existing concepts. The algorithm handles reentrancy and arbitrary cycles
natively, i.e. within the transition system itself. The model is evaluated on
the LDC2015E86 corpus, obtaining results close to the state of the art,
including a Smatch of 64%, and showing good behavior on reentrant edges.
|
Non-projective parsing can be useful to handle cycles and reentrancy in AMR graphs.
|
http://arxiv.org/abs/1805.09007v1
|
http://arxiv.org/pdf/1805.09007v1.pdf
|
NAACL 2018 6
|
[
"David Vilares",
"Carlos Gómez-Rodríguez"
] |
[
"AMR Parsing"
] | 2018-05-23T00:00:00 |
https://aclanthology.org/N18-2023
|
https://aclanthology.org/N18-2023.pdf
|
a-transition-based-algorithm-for-unrestricted-1
| null |
[] |
https://paperswithcode.com/paper/the-description-length-of-deep-learning
|
1802.07044
| null | null |
The Description Length of Deep Learning Models
|
Solomonoff's general theory of inference and the Minimum Description Length
principle formalize Occam's razor, and hold that a good model of data is a
model that is good at losslessly compressing the data, including the cost of
describing the model itself. Deep neural networks might seem to go against this
principle given the large number of parameters to be encoded.
We demonstrate experimentally the ability of deep neural networks to compress
the training data even when accounting for parameter encoding. The compression
viewpoint originally motivated the use of variational methods in neural
networks. Unexpectedly, we found that these variational methods provide
surprisingly poor compression bounds, despite being explicitly built to
minimize such bounds. This might explain the relatively poor practical
performance of variational methods in deep learning. On the other hand, simple
incremental encoding methods yield excellent compression values on deep
networks, vindicating Solomonoff's approach.
| null |
http://arxiv.org/abs/1802.07044v5
|
http://arxiv.org/pdf/1802.07044v5.pdf
|
NeurIPS 2018 12
|
[
"Léonard Blier",
"Yann Ollivier"
] |
[
"Deep Learning"
] | 2018-02-20T00:00:00 |
http://papers.nips.cc/paper/7490-the-description-length-of-deep-learning-models
|
http://papers.nips.cc/paper/7490-the-description-length-of-deep-learning-models.pdf
|
the-description-length-of-deep-learning-1
| null |
[] |
https://paperswithcode.com/paper/gpu-accelerated-cascade-hashing-image
|
1805.08995
| null | null |
GPU Accelerated Cascade Hashing Image Matching for Large Scale 3D Reconstruction
|
Image feature point matching is a key step in Structure from Motion(SFM).
However, it is becoming more and more time consuming because the number of
images is getting larger and larger. In this paper, we proposed a GPU
accelerated image matching method with improved Cascade Hashing. Firstly, we
propose a Disk-Memory-GPU data exchange strategy and optimize the load order of
data, so that the proposed method can deal with big data. Next, we parallelize
the Cascade Hashing method on GPU. An improved parallel reduction and an
improved parallel hashing ranking are proposed to fulfill this task. Finally,
extensive experiments show that our image matching is about 20 times faster
than SiftGPU on the same graphics card, nearly 100 times faster than the CPU
CasHash method and hundreds of times faster than the CPU Kd-Tree based matching
method. Further more, we introduce the epipolar constraint to the proposed
method, and use the epipolar geometry to guide the feature matching procedure,
which further reduces the matching cost.
| null |
http://arxiv.org/abs/1805.08995v1
|
http://arxiv.org/pdf/1805.08995v1.pdf
| null |
[
"Tao Xu",
"Kun Sun",
"Wenbing Tao"
] |
[
"3D Reconstruction",
"CPU",
"GPU"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/data-efficient-hierarchical-reinforcement
|
1805.08296
| null | null |
Data-Efficient Hierarchical Reinforcement Learning
|
Hierarchical reinforcement learning (HRL) is a promising approach to extend
traditional reinforcement learning (RL) methods to solve more complex tasks.
Yet, the majority of current HRL methods require careful task-specific design
and on-policy training, making them difficult to apply in real-world scenarios.
In this paper, we study how we can develop HRL algorithms that are general, in
that they do not make onerous additional assumptions beyond standard RL
algorithms, and efficient, in the sense that they can be used with modest
numbers of interaction samples, making them suitable for real-world problems
such as robotic control. For generality, we develop a scheme where lower-level
controllers are supervised with goals that are learned and proposed
automatically by the higher-level controllers. To address efficiency, we
propose to use off-policy experience for both higher and lower-level training.
This poses a considerable challenge, since changes to the lower-level behaviors
change the action space for the higher-level policy, and we introduce an
off-policy correction to remedy this challenge. This allows us to take
advantage of recent advances in off-policy model-free RL to learn both higher-
and lower-level policies using substantially fewer environment interactions
than on-policy algorithms. We term the resulting HRL agent HIRO and find that
it is generally applicable and highly sample-efficient. Our experiments show
that HIRO can be used to learn highly complex behaviors for simulated robots,
such as pushing objects and utilizing them to reach target locations, learning
from only a few million samples, equivalent to a few days of real-time
interaction. In comparisons with a number of prior HRL methods, we find that
our approach substantially outperforms previous state-of-the-art techniques.
|
In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control.
|
http://arxiv.org/abs/1805.08296v4
|
http://arxiv.org/pdf/1805.08296v4.pdf
|
NeurIPS 2018 12
|
[
"Ofir Nachum",
"Shixiang Gu",
"Honglak Lee",
"Sergey Levine"
] |
[
"Hierarchical Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-21T00:00:00 |
http://papers.nips.cc/paper/7591-data-efficient-hierarchical-reinforcement-learning
|
http://papers.nips.cc/paper/7591-data-efficient-hierarchical-reinforcement-learning.pdf
|
data-efficient-hierarchical-reinforcement-1
| null |
[] |
https://paperswithcode.com/paper/mathcalg-sgd-optimizing-relu-neural-networks
|
1802.03713
| null | null |
$\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
|
It is well known that neural networks with rectified linear units (ReLU) activation functions are positively scale-invariant. Conventional algorithms like stochastic gradient descent optimize the neural networks in the vector space of weights, which is, however, not positively scale-invariant. This mismatch may lead to problems during the optimization process. Then, a natural question is: \emph{can we construct a new vector space that is positively scale-invariant and sufficient to represent ReLU neural networks so as to better facilitate the optimization process }? In this paper, we provide our positive answer to this question. First, we conduct a formal study on the positive scaling operators which forms a transformation group, denoted as $\mathcal{G}$. We show that the value of a path (i.e. the product of the weights along the path) in the neural network is invariant to positive scaling and prove that the value vector of all the paths is sufficient to represent the neural networks under mild conditions. Second, we show that one can identify some basis paths out of all the paths and prove that the linear span of their value vectors (denoted as $\mathcal{G}$-space) is an invariant space with lower dimension under the positive scaling group. Finally, we design stochastic gradient descent algorithm in $\mathcal{G}$-space (abbreviated as $\mathcal{G}$-SGD) to optimize the value vector of the basis paths of neural networks with little extra cost by leveraging back-propagation. Our experiments show that $\mathcal{G}$-SGD significantly outperforms the conventional SGD algorithm in optimizing ReLU networks on benchmark datasets.
| null |
https://arxiv.org/abs/1802.03713v8
|
https://arxiv.org/pdf/1802.03713v8.pdf
| null |
[
"Qi Meng",
"Shuxin Zheng",
"Huishuai Zhang",
"Wei Chen",
"Zhi-Ming Ma",
"Tie-Yan Liu"
] |
[] | 2018-02-11T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/deep-multi-spectral-registration-using
|
1801.05171
| null | null |
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
|
In this paper, we introduce a novel deep-learning method to align
cross-spectral images. Our approach relies on a learned descriptor which is
invariant to different spectra. Multi-modal images of the same scene capture
different signals and therefore their registration is challenging and it is not
solved by classic approaches. To that end, we developed a feature-based
approach that solves the visible (VIS) to Near-Infra-Red (NIR) registration
problem. Our algorithm detects corners by Harris and matches them by a
patch-metric learned on top of CIFAR-10 network descriptor. As our experiments
demonstrate we achieve a high-quality alignment of cross-spectral images with a
sub-pixel accuracy. Comparing to other existing methods, our approach is more
accurate in the task of VIS to NIR registration.
| null |
http://arxiv.org/abs/1801.05171v6
|
http://arxiv.org/pdf/1801.05171v6.pdf
| null |
[
"Nati Ofir",
"Shai Silberstein",
"Hila Levi",
"Dani Rozenbaum",
"Yosi Keller",
"Sharon Duvdevani Bar"
] |
[] | 2018-01-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/self-attention-based-message-relevant
|
1805.08983
| null | null |
Self-Attention-Based Message-Relevant Response Generation for Neural Conversation Model
|
Using a sequence-to-sequence framework, many neural conversation models for
chit-chat succeed in naturalness of the response. Nevertheless, the neural
conversation models tend to give generic responses which are not specific to
given messages, and it still remains as a challenge. To alleviate the tendency,
we propose a method to promote message-relevant and diverse responses for
neural conversation model by using self-attention, which is time-efficient as
well as effective. Furthermore, we present an investigation of why and how
effective self-attention is in deep comparison with the standard dialogue
generation. The experiment results show that the proposed method improves the
standard dialogue generation in various evaluation metrics.
| null |
http://arxiv.org/abs/1805.08983v1
|
http://arxiv.org/pdf/1805.08983v1.pdf
| null |
[
"Jonggu Kim",
"Doyeon Kong",
"Jong-Hyeok Lee"
] |
[
"Dialogue Generation",
"Response Generation"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rgb-t-object-trackingbenchmark-and-baseline
|
1805.08982
| null | null |
RGB-T Object Tracking:Benchmark and Baseline
|
RGB-Thermal (RGB-T) object tracking receives more and more attention due to
the strongly complementary benefits of thermal information to visible data.
However, RGB-T research is limited by lacking a comprehensive evaluation
platform. In this paper, we propose a large-scale video benchmark dataset for
RGB-T tracking.It has three major advantages over existing ones: 1) Its size is
sufficiently large for large-scale performance evaluation (total frame number:
234K, maximum frame per sequence: 8K). 2) The alignment between RGB-T sequence
pairs is highly accurate, which does not need pre- or post-processing. 3) The
occlusion levels are annotated for occlusion-sensitive performance analysis of
different tracking algorithms.Moreover, we propose a novel graph-based approach
to learn a robust object representation for RGB-T tracking. In particular, the
tracked object is represented with a graph with image patches as nodes. This
graph including graph structure, node weights and edge weights is dynamically
learned in a unified ADMM (alternating direction method of multipliers)-based
optimization framework, in which the modality weights are also incorporated for
adaptive fusion of multiple source data.Extensive experiments on the
large-scale dataset are executed to demonstrate the effectiveness of the
proposed tracker against other state-of-the-art tracking methods. We also
provide new insights and potential research directions to the field of RGB-T
object tracking.
|
RGB-Thermal (RGB-T) object tracking receives more and more attention due to the strongly complementary benefits of thermal information to visible data.
|
http://arxiv.org/abs/1805.08982v1
|
http://arxiv.org/pdf/1805.08982v1.pdf
| null |
[
"Chenglong Li",
"Xinyan Liang",
"Yijuan Lu",
"Nan Zhao",
"Jin Tang"
] |
[
"8k",
"Object",
"Object Tracking",
"Rgb-T Tracking"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "The **alternating direction method of multipliers** (**ADMM**) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It takes the form of a decomposition-coordination procedure, in which the solutions to small\r\nlocal subproblems are coordinated to find a solution to a large global problem. ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. It turns out to be equivalent or closely related to many other algorithms\r\nas well, such as Douglas-Rachford splitting from numerical analysis, Spingarn’s method of partial inverses, Dykstra’s alternating projections method, Bregman iterative algorithms for l1 problems in signal processing, proximal methods, and many others.\r\n\r\nText Source: [https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf](https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf)\r\n\r\nImage Source: [here](https://www.slideshare.net/derekcypang/alternating-direction)",
"full_name": "Alternating Direction Method of Multipliers",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Optimization",
"parent": null
},
"name": "ADMM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/single-view-food-portion-estimation-learning
|
1802.09670
| null | null |
Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks
|
Due to the growing concern of chronic diseases and other health problems
related to diet, there is a need to develop accurate methods to estimate an
individual's food and energy intake. Measuring accurate dietary intake is an
open research problem. In particular, accurate food portion estimation is
challenging since the process of food preparation and consumption impose large
variations on food shapes and appearances. In this paper, we present a food
portion estimation method to estimate food energy (kilocalories) from food
images using Generative Adversarial Networks (GAN). We introduce the concept of
an "energy distribution" for each food image. To train the GAN, we design a
food image dataset based on ground truth food labels and segmentation masks for
each food image as well as energy information associated with the food image.
Our goal is to learn the mapping of the food image to the food energy. We can
then estimate food energy based on the energy distribution. We show that an
average energy estimation error rate of 10.89% can be obtained by learning the
image-to-energy mapping.
| null |
http://arxiv.org/abs/1802.09670v2
|
http://arxiv.org/pdf/1802.09670v2.pdf
| null |
[
"Shaobo Fang",
"Zeman Shao",
"Runyu Mao",
"Chichen Fu",
"Deborah A. Kerr",
"Carol J. Boushey",
"Edward J. Delp",
"Fengqing Zhu"
] |
[] | 2018-02-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/a-unified-view-of-causal-and-non-causal
|
1802.05844
| null | null |
A Unified View of Causal and Non-causal Feature Selection
|
In this paper, we aim to develop a unified view of causal and non-causal
feature selection methods. The unified view will fill in the gap in the
research of the relation between the two types of methods. Based on the
Bayesian network framework and information theory, we first show that causal
and non-causal feature selection methods share the same objective. That is to
find the Markov blanket of a class attribute, the theoretically optimal feature
set for classification. We then examine the assumptions made by causal and
non-causal feature selection methods when searching for the optimal feature
set, and unify the assumptions by mapping them to the restrictions on the
structure of the Bayesian network model of the studied problem. We further
analyze in detail how the structural assumptions lead to the different levels
of approximations employed by the methods in their search, which then result in
the approximations in the feature sets found by the methods with respect to the
optimal feature set. With the unified view, we are able to interpret the output
of non-causal methods from a causal perspective and derive the error bounds of
both types of methods. Finally, we present practical understanding of the
relation between causal and non-causal methods using extensive experiments with
synthetic data and various types of real-word data.
| null |
http://arxiv.org/abs/1802.05844v4
|
http://arxiv.org/pdf/1802.05844v4.pdf
| null |
[
"Kui Yu",
"Lin Liu",
"Jiuyong Li"
] |
[
"Attribute",
"feature selection"
] | 2018-02-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/differentiating-the-multipoint-expected
|
1503.05509
| null | null |
Differentiating the multipoint Expected Improvement for optimal batch design
|
This work deals with parallel optimization of expensive objective functions which are modeled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis' formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batch-sequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.
| null |
https://arxiv.org/abs/1503.05509v4
|
https://arxiv.org/pdf/1503.05509v4.pdf
| null |
[
"Sébastien Marmin",
"Clément Chevalier",
"David Ginsbourger"
] |
[
"Bayesian Optimization",
"Gaussian Processes"
] | 2015-03-18T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.\r\n\r\nImage Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams",
"full_name": "Gaussian Process",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "Gaussian Process",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/particle-filter-networks-with-application-to
|
1805.08975
| null | null |
Particle Filter Networks with Application to Visual Localization
|
Particle filtering is a powerful approach to sequential state estimation and
finds application in many domains, including robot localization, object
tracking, etc. To apply particle filtering in practice, a critical challenge is
to construct probabilistic system models, especially for systems with complex
dynamics or rich sensory inputs such as camera images. This paper introduces
the Particle Filter Network (PFnet), which encodes both a system model and a
particle filter algorithm in a single neural network. The PF-net is fully
differentiable and trained end-to-end from data. Instead of learning a generic
system model, it learns a model optimized for the particle filter algorithm. We
apply the PF-net to a visual localization task, in which a robot must localize
in a rich 3-D world, using only a schematic 2-D floor map. In simulation
experiments, PF-net consistently outperforms alternative learning
architectures, as well as a traditional model-based method, under a variety of
sensor inputs. Further, PF-net generalizes well to new, unseen environments.
|
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc.
|
http://arxiv.org/abs/1805.08975v3
|
http://arxiv.org/pdf/1805.08975v3.pdf
| null |
[
"Peter Karkus",
"David Hsu",
"Wee Sun Lee"
] |
[
"Object Tracking",
"State Estimation",
"Visual Localization"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/do-better-imagenet-models-transfer-better
|
1805.08974
| null | null |
Do Better ImageNet Models Transfer Better?
|
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy ($r = 0.99$ and $0.96$, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield penultimate layer features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.
| null |
https://arxiv.org/abs/1805.08974v3
|
https://arxiv.org/pdf/1805.08974v3.pdf
|
CVPR 2019 6
|
[
"Simon Kornblith",
"Jonathon Shlens",
"Quoc V. Le"
] |
[
"Fine-Grained Image Classification",
"General Classification",
"image-classification",
"Image Classification",
"Transfer Learning"
] | 2018-05-23T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper.pdf
|
do-better-imagenet-models-transfer-better-1
| null |
[] |
https://paperswithcode.com/paper/toward-a-thinking-microscope-deep-learning-in
|
1805.08970
| null | null |
Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction
|
We discuss recently emerging applications of the state-of-art deep learning
methods on optical microscopy and microscopic image reconstruction, which
enable new transformations among different modes and modalities of microscopic
imaging, driven entirely by image data. We believe that deep learning will
fundamentally change both the hardware and image reconstruction methods used in
optical microscopy in a holistic manner.
| null |
http://arxiv.org/abs/1805.08970v1
|
http://arxiv.org/pdf/1805.08970v1.pdf
| null |
[
"Yair Rivenson",
"Aydogan Ozcan"
] |
[
"Deep Learning",
"Image Reconstruction"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-network-interpretation-via-fine
|
1805.08969
| null | null |
Semantic Network Interpretation
|
Network interpretation as an effort to reveal the features learned by a network remains largely visualization-based. In this paper, our goal is to tackle semantic network interpretation at both filter and decision level. For filter-level interpretation, we represent the concepts a filter encodes with a probability distribution of visual attributes. The decision-level interpretation is achieved by textual summarization that generates an explanatory sentence containing clues behind a network's decision. A Bayesian inference algorithm is proposed to automatically associate filters and network decisions with visual attributes. Human study confirms that the semantic interpretation is a beneficial alternative or complement to visualization methods. We demonstrate the crucial role that semantic network interpretation can play in understanding a network's failure patterns. More importantly, semantic network interpretation enables a better understanding of the correlation between a model's performance and its distribution metrics like filter selectivity and concept sparseness.
| null |
https://arxiv.org/abs/1805.08969v3
|
https://arxiv.org/pdf/1805.08969v3.pdf
| null |
[
"Pei Guo",
"Ryan Farrell"
] |
[
"Network Interpretation",
"Sentence"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/discovering-blind-spots-in-reinforcement
|
1805.08966
| null | null |
Discovering Blind Spots in Reinforcement Learning
|
Agents trained in simulation may make errors in the real world due to
mismatches between training and execution environments. These mistakes can be
dangerous and difficult to discover because the agent cannot predict them a
priori. We propose using oracle feedback to learn a predictive model of these
blind spots to reduce costly errors in real-world applications. We focus on
blind spots in reinforcement learning (RL) that occur due to incomplete state
representation: The agent does not have the appropriate features to represent
the true state of the world and thus cannot distinguish among numerous states.
We formalize the problem of discovering blind spots in RL as a noisy supervised
learning problem with class imbalance. We learn models to predict blind spots
in unseen regions of the state space by combining techniques for label
aggregation, calibration, and supervised learning. The models take into
consideration noise emerging from different forms of oracle feedback, including
demonstrations and corrections. We evaluate our approach on two domains and
show that it achieves higher predictive performance than baseline methods, and
that the learned model can be used to selectively query an oracle at execution
time to prevent errors. We also empirically analyze the biases of various
feedback types and how they influence the discovery of blind spots.
| null |
http://arxiv.org/abs/1805.08966v1
|
http://arxiv.org/pdf/1805.08966v1.pdf
| null |
[
"Ramya Ramakrishnan",
"Ece Kamar",
"Debadeepta Dey",
"Julie Shah",
"Eric Horvitz"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3d-human-pose-estimation-with-relational
|
1805.08961
| null | null |
3D Human Pose Estimation with Relational Networks
|
In this paper, we propose a novel 3D human pose estimation algorithm from a
single image based on neural networks. We adopted the structure of the
relational networks in order to capture the relations among different body
parts. In our method, each pair of different body parts generates features, and
the average of the features from all the pairs are used for 3D pose estimation.
In addition, we propose a dropout method that can be used in relational
modules, which inherently imposes robustness to the occlusions. The proposed
network achieves state-of-the-art performance for 3D pose estimation in Human
3.6M dataset, and it effectively produces plausible results even in the
existence of missing joints.
|
In this paper, we propose a novel 3D human pose estimation algorithm from a single image based on neural networks.
|
http://arxiv.org/abs/1805.08961v2
|
http://arxiv.org/pdf/1805.08961v2.pdf
| null |
[
"Sungheon Park",
"Nojun Kwak"
] |
[
"3D Human Pose Estimation",
"3D Pose Estimation",
"Pose Estimation"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
}
] |
https://paperswithcode.com/paper/icadx-interpretable-computer-aided-diagnosis
|
1805.08960
| null | null |
ICADx: Interpretable computer aided diagnosis of breast masses
|
In this study, a novel computer aided diagnosis (CADx) framework is devised
to investigate interpretability for classifying breast masses. Recently, a deep
learning technology has been successfully applied to medical image analysis
including CADx. Existing deep learning based CADx approaches, however, have a
limitation in explaining the diagnostic decision. In real clinical practice,
clinical decisions could be made with reasonable explanation. So current deep
learning approaches in CADx are limited in real world deployment. In this
paper, we investigate interpretability in CADx with the proposed interpretable
CADx (ICADx) framework. The proposed framework is devised with a generative
adversarial network, which consists of interpretable diagnosis network and
synthetic lesion generative network to learn the relationship between
malignancy and a standardized description (BI-RADS). The lesion generative
network and the interpretable diagnosis network compete in an adversarial
learning so that the two networks are improved. The effectiveness of the
proposed method was validated on public mammogram database. Experimental
results showed that the proposed ICADx framework could provide the
interpretability of mass as well as mass classification. It was mainly
attributed to the fact that the proposed method was effectively trained to find
the relationship between malignancy and interpretations via the adversarial
learning. These results imply that the proposed ICADx framework could be a
promising approach to develop the CADx system.
| null |
http://arxiv.org/abs/1805.08960v1
|
http://arxiv.org/pdf/1805.08960v1.pdf
| null |
[
"Seong Tae Kim",
"Hakmin Lee",
"Hak Gu Kim",
"Yong Man Ro"
] |
[
"Deep Learning",
"Diagnostic",
"Generative Adversarial Network",
"Medical Image Analysis"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Interpretability",
"source_title": "CAM: Causal additive models, high-dimensional order search and penalized regression",
"source_url": "http://arxiv.org/abs/1310.1533v2"
}
] |
https://paperswithcode.com/paper/a-brand-level-ranking-system-with-the
|
1805.08958
| null | null |
A Brand-level Ranking System with the Customized Attention-GRU Model
|
In e-commerce websites like Taobao, brand is playing a more important role in
influencing users' decision of click/purchase, partly because users are now
attaching more importance to the quality of products and brand is an indicator
of quality. However, existing ranking systems are not specifically designed to
satisfy this kind of demand. Some design tricks may partially alleviate this
problem, but still cannot provide satisfactory results or may create additional
interaction cost. In this paper, we design the first brand-level ranking system
to address this problem. The key challenge of this system is how to
sufficiently exploit users' rich behavior in e-commerce websites to rank the
brands. In our solution, we firstly conduct the feature engineering
specifically tailored for the personalized brand ranking problem and then rank
the brands by an adapted Attention-GRU model containing three important
modifications. Note that our proposed modifications can also apply to many
other machine learning models on various tasks. We conduct a series of
experiments to evaluate the effectiveness of our proposed ranking model and
test the response to the brand-level ranking system from real users on a
large-scale e-commerce platform, i.e. Taobao.
| null |
http://arxiv.org/abs/1805.08958v2
|
http://arxiv.org/pdf/1805.08958v2.pdf
| null |
[
"Yu Zhu",
"Junxiong Zhu",
"Jie Hou",
"Yongliang Li",
"Beidou Wang",
"Ziyu Guan",
"Deng Cai"
] |
[
"Feature Engineering"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-discrete-bayesian-networks-in
|
1803.04087
| null | null |
Learning discrete Bayesian networks in polynomial time and sample complexity
|
In this paper, we study the problem of structure learning for Bayesian
networks in which nodes take discrete values. The problem is NP-hard in general
but we show that under certain conditions we can recover the true structure of
a Bayesian network with sufficient number of samples. We develop a mathematical
model which does not assume any specific conditional probability distributions
for the nodes. We use a primal-dual witness construction to prove that, under
some technical conditions on the interaction between node pairs, we can do
exact recovery of the parents and children of a node by performing group
l_12-regularized multivariate regression. Thus, we recover the true Bayesian
network structure. If degree of a node is bounded then the sample complexity of
our proposed approach grows logarithmically with respect to the number of nodes
in the Bayesian network. Furthermore, our method runs in polynomial time.
| null |
http://arxiv.org/abs/1803.04087v3
|
http://arxiv.org/pdf/1803.04087v3.pdf
| null |
[
"Adarsh Barik",
"Jean Honorio"
] |
[] | 2018-03-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semi-supervised-learning-with-gans-revisiting
|
1805.08957
| null | null |
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
|
GANS are powerful generative models that are able to model the manifold of
natural images. We leverage this property to perform manifold regularization by
approximating the Laplacian norm using a Monte Carlo approximation that is
easily computed with the GAN. When incorporated into the feature-matching GAN
of Improved GAN, we achieve state-of-the-art results for GAN-based
semi-supervised learning on the CIFAR-10 dataset, with a method that is
significantly easier to implement than competing methods.
|
GANS are powerful generative models that are able to model the manifold of natural images.
|
http://arxiv.org/abs/1805.08957v1
|
http://arxiv.org/pdf/1805.08957v1.pdf
| null |
[
"Bruno Lecouat",
"Chuan-Sheng Foo",
"Houssam Zenati",
"Vijay R. Chandrasekhar"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/hypergraph-spectral-clustering-in-the
|
1805.08956
| null | null |
Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
|
Spectral clustering is a celebrated algorithm that partitions objects based
on pairwise similarity information. While this approach has been successfully
applied to a variety of domains, it comes with limitations. The reason is that
there are many other applications in which only \emph{multi}-way similarity
measures are available. This motivates us to explore the multi-way measurement
setting. In this work, we develop two algorithms intended for such setting:
Hypergraph Spectral Clustering (HSC) and Hypergraph Spectral Clustering with
Local Refinement (HSCLR). Our main contribution lies in performance analysis of
the poly-time algorithms under a random hypergraph model, which we name the
weighted stochastic block model, in which objects and multi-way measures are
modeled as nodes and weights of hyperedges, respectively. Denoting by $n$ the
number of nodes, our analysis reveals the following: (1) HSC outputs a
partition which is better than a random guess if the sum of edge weights (to be
explained later) is $\Omega(n)$; (2) HSC outputs a partition which coincides
with the hidden partition except for a vanishing fraction of nodes if the sum
of edge weights is $\omega(n)$; and (3) HSCLR exactly recovers the hidden
partition if the sum of edge weights is on the order of $n \log n$. Our results
improve upon the state of the arts recently established under the model and
they firstly settle the order-wise optimal results for the binary edge weight
case. Moreover, we show that our results lead to efficient sketching algorithms
for subspace clustering, a computer vision application. Lastly, we show that
HSCLR achieves the information-theoretic limits for a special yet practically
relevant model, thereby showing no computational barrier for the case.
| null |
http://arxiv.org/abs/1805.08956v1
|
http://arxiv.org/pdf/1805.08956v1.pdf
| null |
[
"Kwangjun Ahn",
"Kangwook Lee",
"Changho Suh"
] |
[
"Clustering",
"Stochastic Block Model"
] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "Spectral clustering has attracted increasing attention due to\r\nthe promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,",
"full_name": "Spectral Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "Spectral Clustering",
"source_title": "A Tutorial on Spectral Clustering",
"source_url": "http://arxiv.org/abs/0711.0189v1"
}
] |
https://paperswithcode.com/paper/global-model-interpretation-via-recursive
|
1802.04253
| null | null |
Global Model Interpretation via Recursive Partitioning
|
In this work, we propose a simple but effective method to interpret black-box
machine learning models globally. That is, we use a compact binary tree, the
interpretation tree, to explicitly represent the most important decision rules
that are implicitly contained in the black-box machine learning models. This
tree is learned from the contribution matrix which consists of the
contributions of input variables to predicted scores for each single
prediction. To generate the interpretation tree, a unified process recursively
partitions the input variable space by maximizing the difference in the average
contribution of the split variable between the divided spaces. We demonstrate
the effectiveness of our method in diagnosing machine learning models on
multiple tasks. Also, it is useful for new knowledge discovery as such insights
are not easily identifiable when only looking at single predictions. In
general, our work makes it easier and more efficient for human beings to
understand machine learning models.
|
To generate the interpretation tree, a unified process recursively partitions the input variable space by maximizing the difference in the average contribution of the split variable between the divided spaces.
|
http://arxiv.org/abs/1802.04253v2
|
http://arxiv.org/pdf/1802.04253v2.pdf
| null |
[
"Chengliang Yang",
"Anand Rangarajan",
"Sanjay Ranka"
] |
[
"BIG-bench Machine Learning",
"model"
] | 2018-02-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dictionary-learning-by-dynamical-neural
|
1805.08952
| null | null |
Dictionary Learning by Dynamical Neural Networks
|
A dynamical neural network consists of a set of interconnected neurons that
interact over time continuously. It can exhibit computational properties in the
sense that the dynamical system's evolution and/or limit points in the
associated state space can correspond to numerical solutions to certain
mathematical optimization or learning problems. Such a computational system is
particularly attractive in that it can be mapped to a massively parallel
computer architecture for power and throughput efficiency, especially if each
neuron can rely solely on local information (i.e., local memory). Deriving
gradients from the dynamical network's various states while conforming to this
last constraint, however, is challenging. We show that by combining ideas of
top-down feedback and contrastive learning, a dynamical network for solving the
l1-minimizing dictionary learning problem can be constructed, and the true
gradients for learning are provably computable by individual neurons. Using
spiking neurons to construct our dynamical network, we present a learning
process, its rigorous mathematical analysis, and numerical results on several
dictionary learning problems.
| null |
http://arxiv.org/abs/1805.08952v1
|
http://arxiv.org/pdf/1805.08952v1.pdf
| null |
[
"Tsung-Han Lin",
"Ping Tak Peter Tang"
] |
[
"Contrastive Learning",
"Dictionary Learning"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/minimax-distribution-estimation-in
|
1802.08855
| null | null |
Minimax Distribution Estimation in Wasserstein Distance
|
The Wasserstein metric is an important measure of distance between probability distributions, with applications in machine learning, statistics, probability theory, and data analysis. This paper provides upper and lower bounds on statistical minimax rates for the problem of estimating a probability distribution under Wasserstein loss, using only metric properties, such as covering and packing numbers, of the sample space, and weak moment assumptions on the probability distributions.
| null |
https://arxiv.org/abs/1802.08855v3
|
https://arxiv.org/pdf/1802.08855v3.pdf
| null |
[
"Shashank Singh",
"Barnabás Póczos"
] |
[
"BIG-bench Machine Learning"
] | 2018-02-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-to-mine-aligned-code-and-natural
|
1805.08949
| null | null |
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
|
For tasks like code synthesis from natural language, code retrieval, and code
summarization, data-driven models have shown great promise. However, creating
these models require parallel data between natural language (NL) and code with
fine-grained alignments. Stack Overflow (SO) is a promising source to create
such a data set: the questions are diverse and most of them have corresponding
answers with high-quality code snippets. However, existing heuristic methods
(e.g., pairing the title of a post with the code in the accepted answer) are
limited both in their coverage and the correctness of the NL-code pairs
obtained. In this paper, we propose a novel method to mine high-quality aligned
data from SO using two sets of features: hand-crafted features considering the
structure of the extracted snippets, and correspondence features obtained by
training a probabilistic model to capture the correlation between NL and code
using neural networks. These features are fed into a classifier that determines
the quality of mined NL-code pairs. Experiments using Python and Java as test
beds show that the proposed method greatly expands coverage and accuracy over
existing mining methods, even when using only a small number of labeled
examples. Further, we find that reasonable results are achieved even when
training the classifier on one language and testing on another, showing promise
for scaling NL-code mining to a wide variety of programming languages beyond
those for which we are able to annotate data.
| null |
http://arxiv.org/abs/1805.08949v1
|
http://arxiv.org/pdf/1805.08949v1.pdf
| null |
[
"Pengcheng Yin",
"Bowen Deng",
"Edgar Chen",
"Bogdan Vasilescu",
"Graham Neubig"
] |
[
"Code Summarization",
"Retrieval",
"Source Code Summarization"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scalable-coordinated-exploration-in
|
1805.08948
| null | null |
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
|
We consider a team of reinforcement learning agents that concurrently operate
in a common environment, and we develop an approach to efficient coordinated
exploration that is suitable for problems of practical scale. Our approach
builds on seed sampling (Dimakopoulou and Van Roy, 2018) and randomized value
function learning (Osband et al., 2016). We demonstrate that, for simple
tabular contexts, the approach is competitive with previously proposed tabular
model learning methods (Dimakopoulou and Van Roy, 2018). With a
higher-dimensional problem and a neural network value function representation,
the approach learns quickly with far fewer agents than alternative exploration
schemes.
|
We consider a team of reinforcement learning agents that concurrently operate in a common environment, and we develop an approach to efficient coordinated exploration that is suitable for problems of practical scale.
|
http://arxiv.org/abs/1805.08948v2
|
http://arxiv.org/pdf/1805.08948v2.pdf
|
NeurIPS 2018 12
|
[
"Maria Dimakopoulou",
"Ian Osband",
"Benjamin Van Roy"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-23T00:00:00 |
http://papers.nips.cc/paper/7676-scalable-coordinated-exploration-in-concurrent-reinforcement-learning
|
http://papers.nips.cc/paper/7676-scalable-coordinated-exploration-in-concurrent-reinforcement-learning.pdf
|
scalable-coordinated-exploration-in-1
| null |
[] |
https://paperswithcode.com/paper/building-extraction-at-scale-using
|
1805.08946
| null | null |
Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States
|
Establishing up-to-date large scale building maps is essential to understand
urban dynamics, such as estimating population, urban planning and many other
applications. Although many computer vision tasks has been successfully carried
out with deep convolutional neural networks, there is a growing need to
understand their large scale impact on building mapping with remote sensing
imagery. Taking advantage of the scalability of CNNs and using only few areas
with the abundance of building footprints, for the first time we conduct a
comparative analysis of four state-of-the-art CNNs for extracting building
footprints across the entire continental United States. The four CNN
architectures namely: branch-out CNN, fully convolutional neural network (FCN),
conditional random field as recurrent neural network (CRFasRNN), and SegNet,
support semantic pixel-wise labeling and focus on capturing textural
information at multi-scale. We use 1-meter resolution aerial images from
National Agriculture Imagery Program (NAIP) as the test-bed, and compare the
extraction results across the four methods. In addition, we propose to combine
signed-distance labels with SegNet, the preferred CNN architecture identified
by our extensive evaluations, to advance building extraction results to
instance level. We further demonstrate the usefulness of fusing additional near
IR information into the building extraction framework. Large scale experimental
evaluations are conducted and reported using metrics that include: precision,
recall rate, intersection over union, and the number of buildings extracted.
With the improved CNN model and no requirement of further post-processing, we
have generated building maps for the United States. The quality of extracted
buildings and processing time demonstrated the proposed CNN-based framework
fits the need of building extraction at scale.
| null |
http://arxiv.org/abs/1805.08946v1
|
http://arxiv.org/pdf/1805.08946v1.pdf
| null |
[
"Hsiuhan Lexie Yang",
"Jiangye Yuan",
"Dalton Lunga",
"Melanie Laverdiere",
"Amy Rose",
"Budhendra Bhaduri"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/yassouali/pytorch_segmentation/blob/8b8e3ee20a3aa733cb19fc158ad5d7773ed6da7f/models/segnet.py#L9",
"description": "**SegNet** is a semantic segmentation model. This core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the\r\nVGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature maps. Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to\r\nperform non-linear upsampling.",
"full_name": "SegNet",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "SegNet",
"source_title": "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation",
"source_url": "http://arxiv.org/abs/1511.00561v3"
}
] |
https://paperswithcode.com/paper/shared-autonomy-via-deep-reinforcement
|
1802.01744
| null | null |
Shared Autonomy via Deep Reinforcement Learning
|
In shared autonomy, user input is combined with semi-autonomous control to
achieve a common goal. The goal is often unknown ex-ante, so prior work enables
agents to infer the goal from user input and assist with the task. Such methods
tend to assume some combination of knowledge of the dynamics of the
environment, the user's policy given their goal, and the set of possible goals
the user might target, which limits their application to real-world scenarios.
We propose a deep reinforcement learning framework for model-free shared
autonomy that lifts these assumptions. We use human-in-the-loop reinforcement
learning with neural network function approximation to learn an end-to-end
mapping from environmental observation and user input to agent action values,
with task reward as the only form of supervision. This approach poses the
challenge of following user commands closely enough to provide the user with
real-time action feedback and thereby ensure high-quality user input, but also
deviating from the user's actions when they are suboptimal. We balance these
two needs by discarding actions whose values fall below some threshold, then
selecting the remaining action closest to the user's input. Controlled studies
with users (n = 12) and synthetic pilots playing a video game, and a pilot
study with users (n = 4) flying a real quadrotor, demonstrate the ability of
our algorithm to assist users with real-time control tasks in which the agent
cannot directly access the user's private information through observations, but
receives a reward signal and user input that both depend on the user's intent.
The agent learns to assist the user without access to this private information,
implicitly inferring it from the user's input. This paper is a proof of concept
that illustrates the potential for deep reinforcement learning to enable
flexible and practical assistive systems.
|
In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal.
|
http://arxiv.org/abs/1802.01744v2
|
http://arxiv.org/pdf/1802.01744v2.pdf
| null |
[
"Siddharth Reddy",
"Anca D. Dragan",
"Sergey Levine"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-02-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/image-matching-an-application-oriented
|
1709.03917
| null | null |
Image Matching: An Application-oriented Benchmark
|
Image matching approaches have been widely used in computer vision
applications in which the image-level matching performance of matchers is
critical. However, it has not been well investigated by previous works which
place more emphases on evaluating local features. To this end, we present a
uniform benchmark with novel evaluation metrics and a large-scale dataset for
evaluating the overall performance of image matching methods. The proposed
metrics are application-oriented as they emphasize application requirements for
matchers. The dataset contains two portions for benchmarking video frame
matching and unordered image matching separately, where each portion consists
of real-world image sequences and each sequence has a specific attribute.
Subsequently, we carry out a comprehensive performance evaluation of different
state-of-the-art methods and conduct in-depth analyses regarding various
aspects such as application requirements, matching types, and data diversity.
Moreover, we shed light on how to choose appropriate approaches for different
applications based on empirical results and analyses. Conclusions in this
benchmark can be used as general guidelines to design practical matching
systems and also advocate potential future research directions in this field.
| null |
http://arxiv.org/abs/1709.03917v4
|
http://arxiv.org/pdf/1709.03917v4.pdf
| null |
[
"Jia-Wang Bian",
"Le Zhang",
"Yun Liu",
"Wen-Yan Lin",
"Ming-Ming Cheng",
"Ian D. Reid"
] |
[
"Attribute",
"Benchmarking",
"Diversity"
] | 2017-09-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/approximate-random-dropout
|
1805.08939
| null | null |
Approximate Random Dropout
|
The training phases of Deep neural network~(DNN) consumes enormous processing
time and energy. Compression techniques utilizing the sparsity of DNNs can
effectively accelerate the inference phase of DNNs. However, it can be hardly
used in the training phase because the training phase involves dense
matrix-multiplication using General Purpose Computation on Graphics Processors
(GPGPU), which endorse regular and structural data layout. In this paper, we
propose the Approximate Random Dropout that replaces the conventional random
dropout of neurons and synapses with a regular and predefined patterns to
eliminate the unnecessary computation and data access. To compensate the
potential performance loss we develop a SGD-based Search Algorithm to produce
the distribution of dropout patterns. We prove our approach is statistically
equivalent to the previous dropout method. Experiments results on MLP and LSTM
using well-known benchmarks show that the proposed Approximate Random Dropout
can reduce the training time by $20\%$-$77\%$ ($19\%$-$60\%$) when dropout rate
is $0.3$-$0.7$ on MLP (LSTM) with marginal accuracy drop.
| null |
http://arxiv.org/abs/1805.08939v2
|
http://arxiv.org/pdf/1805.08939v2.pdf
| null |
[
"Zhuoran Song",
"Ru Wang",
"Dongyu Ru",
"Hongru Huang",
"Zhenghao Peng",
"Jing Ke",
"Xiaoyao Liang",
"Li Jiang"
] |
[] | 2018-05-23T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
}
] |
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