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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/why-do-deep-convolutional-networks-generalize
|
1805.12177
| null |
HJxYwiC5tm
|
Why do deep convolutional networks generalize so poorly to small image transformations?
|
Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation. Recently, several authors have shown that this is not the case: small translations or rescalings of the input image can drastically change the network's prediction. In this paper, we quantify this phenomena and ask why neither the convolutional architecture nor data augmentation are sufficient to achieve the desired invariance. Specifically, we show that the convolutional architecture does not give invariance since architectures ignore the classical sampling theorem, and data augmentation does not give invariance because the CNNs learn to be invariant to transformations only for images that are very similar to typical images from the training set. We discuss two possible solutions to this problem: (1) antialiasing the intermediate representations and (2) increasing data augmentation and show that they provide only a partial solution at best. Taken together, our results indicate that the problem of insuring invariance to small image transformations in neural networks while preserving high accuracy remains unsolved.
|
Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation.
|
https://arxiv.org/abs/1805.12177v4
|
https://arxiv.org/pdf/1805.12177v4.pdf
|
ICLR 2019 5
|
[
"Aharon Azulay",
"Yair Weiss"
] |
[
"Data Augmentation",
"Object Recognition"
] | 2018-05-30T00:00:00 |
https://openreview.net/forum?id=HJxYwiC5tm
|
https://openreview.net/pdf?id=HJxYwiC5tm
|
why-do-deep-convolutional-networks-generalize-1
| null |
[] |
https://paperswithcode.com/paper/deep-segment-hash-learning-for-music
|
1805.12176
| null | null |
Deep Segment Hash Learning for Music Generation
|
Music generation research has grown in popularity over the past decade,
thanks to the deep learning revolution that has redefined the landscape of
artificial intelligence. In this paper, we propose a novel approach to music
generation inspired by musical segment concatenation methods and hash learning
algorithms. Given a segment of music, we use a deep recurrent neural network
and ranking-based hash learning to assign a forward hash code to the segment to
retrieve candidate segments for continuation with matching backward hash codes.
The proposed method is thus called Deep Segment Hash Learning (DSHL). To the
best of our knowledge, DSHL is the first end-to-end segment hash learning
method for music generation, and the first to use pair-wise training with
segments of music. We demonstrate that this method is capable of generating
music which is both original and enjoyable, and that DSHL offers a promising
new direction for music generation research.
| null |
http://arxiv.org/abs/1805.12176v1
|
http://arxiv.org/pdf/1805.12176v1.pdf
| null |
[
"Kevin Joslyn",
"Naifan Zhuang",
"Kien A. Hua"
] |
[
"Music Generation"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-video-summarization-using-unpaired
|
1805.12174
| null | null |
Video Summarization by Learning from Unpaired Data
|
We consider the problem of video summarization. Given an input raw video, the
goal is to select a small subset of key frames from the input video to create a
shorter summary video that best describes the content of the original video.
Most of the current state-of-the-art video summarization approaches use
supervised learning and require labeled training data. Each training instance
consists of a raw input video and its ground truth summary video curated by
human annotators. However, it is very expensive and difficult to create such
labeled training examples. To address this limitation, we propose a novel
formulation to learn video summarization from unpaired data. We present an
approach that learns to generate optimal video summaries using a set of raw
videos ($V$) and a set of summary videos ($S$), where there exists no
correspondence between $V$ and $S$. We argue that this type of data is much
easier to collect. Our model aims to learn a mapping function $F : V
\rightarrow S$ such that the distribution of resultant summary videos from
$F(V)$ is similar to the distribution of $S$ with the help of an adversarial
objective. In addition, we enforce a diversity constraint on $F(V)$ to ensure
that the generated video summaries are visually diverse. Experimental results
on two benchmark datasets indicate that our proposed approach significantly
outperforms other alternative methods.
| null |
http://arxiv.org/abs/1805.12174v2
|
http://arxiv.org/pdf/1805.12174v2.pdf
|
CVPR 2019 6
|
[
"Mrigank Rochan",
"Yang Wang"
] |
[
"Video Summarization"
] | 2018-05-30T00:00:00 |
http://openaccess.thecvf.com/content_CVPR_2019/html/Rochan_Video_Summarization_by_Learning_From_Unpaired_Data_CVPR_2019_paper.html
|
http://openaccess.thecvf.com/content_CVPR_2019/papers/Rochan_Video_Summarization_by_Learning_From_Unpaired_Data_CVPR_2019_paper.pdf
|
video-summarization-by-learning-from-unpaired
| null |
[] |
https://paperswithcode.com/paper/a-flexible-framework-for-multi-objective
|
1805.12168
| null | null |
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
|
Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives. However, in certain cases a practitioner might desire to identify Pareto optimal points only in a subset of the Pareto front due to external considerations. In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem. Our approach is able to flexibly sample from desired regions of the Pareto front and, computationally, is considerably cheaper than most approaches for MOO. We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret. We experiment with both synthetic and real-life problems, and demonstrate superior performance of our proposed algorithm in terms of the flexibility and regret.
| null |
https://arxiv.org/abs/1805.12168v3
|
https://arxiv.org/pdf/1805.12168v3.pdf
| null |
[
"Biswajit Paria",
"Kirthevasan Kandasamy",
"Barnabás Póczos"
] |
[
"Bayesian Optimization"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/supervised-mixed-norm-autoencoder-for-kinship
|
1805.12167
| null | null |
Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos
|
Identifying kinship relations has garnered interest due to several
applications such as organizing and tagging the enormous amount of videos being
uploaded on the Internet. Existing research in kinship verification primarily
focuses on kinship prediction with image pairs. In this research, we propose a
new deep learning framework for kinship verification in unconstrained videos
using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This
new autoencoder formulation introduces class-specific sparsity in the weight
matrix. The proposed three-stage SMNAE based kinship verification framework
utilizes the learned spatio-temporal representation in the video frames for
verifying kinship in a pair of videos. A new kinship video (KIVI) database of
more than 500 individuals with variations due to illumination, pose, occlusion,
ethnicity, and expression is collected for this research. It comprises a total
of 355 true kin video pairs with over 250,000 still frames. The effectiveness
of the proposed framework is demonstrated on the KIVI database and six existing
kinship databases. On the KIVI database, SMNAE yields video-based kinship
verification accuracy of 83.18% which is at least 3.2% better than existing
algorithms. The algorithm is also evaluated on six publicly available kinship
databases and compared with best-reported results. It is observed that the
proposed SMNAE consistently yields best results on all the databases
| null |
http://arxiv.org/abs/1805.12167v1
|
http://arxiv.org/pdf/1805.12167v1.pdf
| null |
[
"Naman Kohli",
"Daksha Yadav",
"Mayank Vatsa",
"Richa Singh",
"Afzel Noore"
] |
[
"Kinship Verification"
] | 2018-05-30T00:00:00 | null | null | null | 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/what-the-vec-towards-probabilistically
|
1805.12164
| null | null |
What the Vec? Towards Probabilistically Grounded Embeddings
|
Word2Vec (W2V) and GloVe are popular, fast and efficient word embedding algorithms. Their embeddings are widely used and perform well on a variety of natural language processing tasks. Moreover, W2V has recently been adopted in the field of graph embedding, where it underpins several leading algorithms. However, despite their ubiquity and relatively simple model architecture, a theoretical understanding of what the embedding parameters of W2V and GloVe learn and why that is useful in downstream tasks has been lacking. We show that different interactions between PMI vectors reflect semantic word relationships, such as similarity and paraphrasing, that are encoded in low dimensional word embeddings under a suitable projection, theoretically explaining why embeddings of W2V and GloVe work. As a consequence, we also reveal an interesting mathematical interconnection between the considered semantic relationships themselves.
| null |
https://arxiv.org/abs/1805.12164v3
|
https://arxiv.org/pdf/1805.12164v3.pdf
|
NeurIPS 2019 12
|
[
"Carl Allen",
"Ivana Balažević",
"Timothy Hospedales"
] |
[
"Graph Embedding",
"Word Embeddings"
] | 2018-05-30T00:00:00 |
http://papers.nips.cc/paper/8965-what-the-vec-towards-probabilistically-grounded-embeddings
|
http://papers.nips.cc/paper/8965-what-the-vec-towards-probabilistically-grounded-embeddings.pdf
|
what-the-vec-towards-probabilistically-1
| null |
[
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two words and the logarithm of their number of co-occurrences:\r\n\r\n$$ J=\\sum\\_{i, j=1}^{V}f\\left(𝑋\\_{i j}\\right)(w^{T}\\_{i}\\tilde{w}_{j} + b\\_{i} + \\tilde{b}\\_{j} - \\log{𝑋}\\_{ij})^{2} $$\r\n\r\nwhere $w\\_{i}$ and $b\\_{i}$ are the word vector and bias respectively of word $i$, $\\tilde{w}_{j}$ and $b\\_{j}$ are the context word vector and bias respectively of word $j$, $X\\_{ij}$ is the number of times word $i$ occurs in the context of word $j$, and $f$ is a weighting function that assigns lower weights to rare and frequent co-occurrences.",
"full_name": "GloVe Embeddings",
"introduced_year": 2000,
"main_collection": {
"area": "Natural Language Processing",
"description": "",
"name": "Word Embeddings",
"parent": null
},
"name": "GloVe",
"source_title": "GloVe: Global Vectors for Word Representation",
"source_url": "https://aclanthology.org/D14-1162"
}
] |
https://paperswithcode.com/paper/on-consensus-optimality-trade-offs-in
|
1805.12120
| null | null |
On Consensus-Optimality Trade-offs in Collaborative Deep Learning
|
In distributed machine learning, where agents collaboratively learn from
diverse private data sets, there is a fundamental tension between consensus and
optimality. In this paper, we build on recent algorithmic progresses in
distributed deep learning to explore various consensus-optimality trade-offs
over a fixed communication topology. First, we propose the incremental
consensus-based distributed SGD (i-CDSGD) algorithm, which involves multiple
consensus steps (where each agent communicates information with its neighbors)
within each SGD iteration. Second, we propose the generalized consensus-based
distributed SGD (g-CDSGD) algorithm that enables us to navigate the full
spectrum from complete consensus (all agents agree) to complete disagreement
(each agent converges to individual model parameters). We analytically
establish convergence of the proposed algorithms for strongly convex and
nonconvex objective functions; we also analyze the momentum variants of the
algorithms for the strongly convex case. We support our algorithms via
numerical experiments, and demonstrate significant improvements over existing
methods for collaborative deep learning.
| null |
http://arxiv.org/abs/1805.12120v1
|
http://arxiv.org/pdf/1805.12120v1.pdf
| null |
[
"Zhanhong Jiang",
"Aditya Balu",
"Chinmay Hegde",
"Soumik Sarkar"
] |
[
"Deep Learning",
"Navigate"
] | 2018-05-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"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": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/neural-models-for-key-phrase-detection-and
|
1706.04560
| null | null |
Neural Models for Key Phrase Detection and Question Generation
|
We propose a two-stage neural model to tackle question generation from
documents. First, our model estimates the probability that word sequences in a
document are ones that a human would pick when selecting candidate answers by
training a neural key-phrase extractor on the answers in a question-answering
corpus. Predicted key phrases then act as target answers and condition a
sequence-to-sequence question-generation model with a copy mechanism.
Empirically, our key-phrase extraction model significantly outperforms an
entity-tagging baseline and existing rule-based approaches. We further
demonstrate that our question generation system formulates fluent, answerable
questions from key phrases. This two-stage system could be used to augment or
generate reading comprehension datasets, which may be leveraged to improve
machine reading systems or in educational settings.
|
We propose a two-stage neural model to tackle question generation from documents.
|
http://arxiv.org/abs/1706.04560v3
|
http://arxiv.org/pdf/1706.04560v3.pdf
| null |
[
"Sandeep Subramanian",
"Tong Wang",
"Xingdi Yuan",
"Saizheng Zhang",
"Yoshua Bengio",
"Adam Trischler"
] |
[
"Question Answering",
"Question Generation",
"Question-Generation",
"Reading Comprehension"
] | 2017-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/amnestic-forgery-an-ontology-of-conceptual
|
1805.12115
| null | null |
Amnestic Forgery: an Ontology of Conceptual Metaphors
|
This paper presents Amnestic Forgery, an ontology for metaphor semantics,
based on MetaNet, which is inspired by the theory of Conceptual Metaphor.
Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology
design framework to deal with both semiotic and referential aspects of frames,
roles, mappings, and eventually blending. The description of the resource is
supplied by a discussion of its applications, with examples taken from metaphor
generation, and the referential problems of metaphoric mappings. Both schema
and data are available from the Framester SPARQL endpoint.
| null |
http://arxiv.org/abs/1805.12115v1
|
http://arxiv.org/pdf/1805.12115v1.pdf
| null |
[
"Aldo Gangemi",
"Mehwish Alam",
"Valentina Presutti"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-reinforcement-learning-in-a-handful-of
|
1805.12114
| null | null |
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
|
Model-based reinforcement learning (RL) algorithms can attain excellent
sample efficiency, but often lag behind the best model-free algorithms in terms
of asymptotic performance. This is especially true with high-capacity
parametric function approximators, such as deep networks. In this paper, we
study how to bridge this gap, by employing uncertainty-aware dynamics models.
We propose a new algorithm called probabilistic ensembles with trajectory
sampling (PETS) that combines uncertainty-aware deep network dynamics models
with sampling-based uncertainty propagation. Our comparison to state-of-the-art
model-based and model-free deep RL algorithms shows that our approach matches
the asymptotic performance of model-free algorithms on several challenging
benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125
times fewer samples than Soft Actor Critic and Proximal Policy Optimization
respectively on the half-cheetah task).
|
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance.
|
http://arxiv.org/abs/1805.12114v2
|
http://arxiv.org/pdf/1805.12114v2.pdf
|
NeurIPS 2018 12
|
[
"Kurtland Chua",
"Roberto Calandra",
"Rowan Mcallister",
"Sergey Levine"
] |
[
"Deep Reinforcement Learning",
"Model-based Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-30T00:00:00 |
http://papers.nips.cc/paper/7725-deep-reinforcement-learning-in-a-handful-of-trials-using-probabilistic-dynamics-models
|
http://papers.nips.cc/paper/7725-deep-reinforcement-learning-in-a-handful-of-trials-using-probabilistic-dynamics-models.pdf
|
deep-reinforcement-learning-in-a-handful-of-1
| null |
[
{
"code_snippet_url": null,
"description": "**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\\_{t} = \\left(s\\_{t}, a\\_{t}, r\\_{t}, s\\_{t+1}\\right)$ in a data-set $D = e\\_{1}, \\cdots, e\\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.\r\n\r\nImage Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)",
"full_name": "Experience Replay",
"introduced_year": 1993,
"main_collection": {
"area": "Reinforcement Learning",
"description": "",
"name": "Replay Memory",
"parent": null
},
"name": "Experience Replay",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": 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
},
{
"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": null,
"description": "**Soft Actor Critic**, or **SAC**, is an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as [Q-learning methods](https://paperswithcode.com/method/q-learning). [SAC](https://paperswithcode.com/method/sac) combines off-policy updates with a stable stochastic actor-critic formulation.\r\n\r\nThe SAC objective has a number of advantages. First, the policy is incentivized to explore more widely, while giving up on clearly unpromising avenues. Second, the policy can capture multiple modes of near-optimal behavior. In problem settings where multiple actions seem equally attractive, the policy will commit equal probability mass to those actions. Lastly, the authors present evidence that it improves learning speed over state-of-art methods that optimize the conventional RL objective function.",
"full_name": "Soft Actor Critic",
"introduced_year": 2000,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "Soft Actor Critic",
"source_title": "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor",
"source_url": "http://arxiv.org/abs/1801.01290v2"
}
] |
https://paperswithcode.com/paper/optimal-combination-of-image-denoisers
|
1711.06712
| null | null |
Optimal Combination of Image Denoisers
|
Given a set of image denoisers, each having a different denoising capability,
is there a provably optimal way of combining these denoisers to produce an
overall better result? An answer to this question is fundamental to designing
an ensemble of weak estimators for complex scenes. In this paper, we present an
optimal combination scheme by leveraging deep neural networks and convex
optimization. The proposed framework, called the Consensus Neural Network
(CsNet), introduces three new concepts in image denoising: (1) A provably
optimal procedure to combine the denoised outputs via convex optimization; (2)
A deep neural network to estimate the mean squared error (MSE) of denoised
images without needing the ground truths; (3) An image boosting procedure using
a deep neural network to improve contrast and to recover lost details of the
combined images. Experimental results show that CsNet can consistently improve
denoising performance for both deterministic and neural network denoisers.
| null |
http://arxiv.org/abs/1711.06712v4
|
http://arxiv.org/pdf/1711.06712v4.pdf
| null |
[
"Joon Hee Choi",
"Omar Elgendy",
"Stanley H. Chan"
] |
[
"Denoising",
"Image Denoising"
] | 2017-11-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/context-aware-cascade-attention-based-rnn-for
|
1805.12098
| null | null |
Context-aware Cascade Attention-based RNN for Video Emotion Recognition
|
Emotion recognition can provide crucial information about the user in many
applications when building human-computer interaction (HCI) systems. Most of
current researches on visual emotion recognition are focusing on exploring
facial features. However, context information including surrounding environment
and human body can also provide extra clues to recognize emotion more
accurately. Inspired by "sequence to sequence model" for neural machine
translation, which models input and output sequences by an encoder and a
decoder in recurrent neural network (RNN) architecture respectively, a novel
architecture, "CACA-RNN", is proposed in this work. The proposed network
consists of two RNNs in a cascaded architecture to process both context and
facial information to perform video emotion classification. Results of the
model were submitted to video emotion recognition sub-challenge in Multimodal
Emotion Recognition Challenge (MEC2017). CACA-RNN outperforms the MEC2017
baseline (mAP of 21.7%): it achieved mAP of 45.51% on the testing set in the
video only challenge.
| null |
http://arxiv.org/abs/1805.12098v1
|
http://arxiv.org/pdf/1805.12098v1.pdf
| null |
[
"Man-Chin Sun",
"Shih-Huan Hsu",
"Min-Chun Yang",
"Jen-Hsien Chien"
] |
[
"Decoder",
"Emotion Classification",
"Emotion Recognition",
"Machine Translation",
"Multimodal Emotion Recognition",
"Translation",
"Video Emotion Recognition"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/infovae-information-maximizing-variational
|
1706.02262
| null | null |
InfoVAE: Information Maximizing Variational Autoencoders
|
A key advance in learning generative models is the use of amortized inference
distributions that are jointly trained with the models. We find that existing
training objectives for variational autoencoders can lead to inaccurate
amortized inference distributions and, in some cases, improving the objective
provably degrades the inference quality. In addition, it has been observed that
variational autoencoders tend to ignore the latent variables when combined with
a decoding distribution that is too flexible. We again identify the cause in
existing training criteria and propose a new class of objectives (InfoVAE) that
mitigate these problems. We show that our model can significantly improve the
quality of the variational posterior and can make effective use of the latent
features regardless of the flexibility of the decoding distribution. Through
extensive qualitative and quantitative analyses, we demonstrate that our models
outperform competing approaches on multiple performance metrics.
|
A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models.
|
http://arxiv.org/abs/1706.02262v3
|
http://arxiv.org/pdf/1706.02262v3.pdf
| null |
[
"Shengjia Zhao",
"Jiaming Song",
"Stefano Ermon"
] |
[] | 2017-06-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/efficient-distributed-semi-supervised
|
1612.04898
| null | null |
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs
|
We describe a computationally efficient, stochastic graph-regularization
technique that can be utilized for the semi-supervised training of deep neural
networks in a parallel or distributed setting. We utilize a technique, first
described in [13] for the construction of mini-batches for stochastic gradient
descent (SGD) based on synthesized partitions of an affinity graph that are
consistent with the graph structure, but also preserve enough stochasticity for
convergence of SGD to good local minima. We show how our technique allows a
graph-based semi-supervised loss function to be decomposed into a sum over
objectives, facilitating data parallelism for scalable training of machine
learning models. Empirical results indicate that our method significantly
improves classification accuracy compared to the fully-supervised case when the
fraction of labeled data is low, and in the parallel case, achieves significant
speed-up in terms of wall-clock time to convergence. We show the results for
both sequential and distributed-memory semi-supervised DNN training on a speech
corpus.
| null |
http://arxiv.org/abs/1612.04898v2
|
http://arxiv.org/pdf/1612.04898v2.pdf
| null |
[
"Sunil Thulasidasan",
"Jeffrey Bilmes",
"Garrett Kenyon"
] |
[] | 2016-12-15T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"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": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/marian-cost-effective-high-quality-neural
|
1805.12096
| null | null |
Marian: Cost-effective High-Quality Neural Machine Translation in C++
|
This paper describes the submissions of the "Marian" team to the WNMT 2018
shared task. We investigate combinations of teacher-student training,
low-precision matrix products, auto-tuning and other methods to optimize the
Transformer model on GPU and CPU. By further integrating these methods with the
new averaging attention networks, a recently introduced faster Transformer
variant, we create a number of high-quality, high-performance models on the GPU
and CPU, dominating the Pareto frontier for this shared task.
|
This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task.
|
http://arxiv.org/abs/1805.12096v1
|
http://arxiv.org/pdf/1805.12096v1.pdf
|
WS 2018 7
|
[
"Marcin Junczys-Dowmunt",
"Kenneth Heafield",
"Hieu Hoang",
"Roman Grundkiewicz",
"Anthony Aue"
] |
[
"CPU",
"GPU",
"Machine Translation",
"Translation",
"Vocal Bursts Intensity Prediction"
] | 2018-05-30T00:00:00 |
https://aclanthology.org/W18-2716
|
https://aclanthology.org/W18-2716.pdf
|
marian-cost-effective-high-quality-neural-1
| null |
[] |
https://paperswithcode.com/paper/generalised-structural-cnns-scnns-for-time
|
1803.05419
| null | null |
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology
|
Deep Learning methods, specifically convolutional neural networks (CNNs),
have seen a lot of success in the domain of image-based data, where the data
offers a clearly structured topology in the regular lattice of pixels. This
4-neighbourhood topological simplicity makes the application of convolutional
masks straightforward for time series data, such as video applications, but
many high-dimensional time series data are not organised in regular lattices,
and instead values may have adjacency relationships with non-trivial
topologies, such as small-world networks or trees. In our application case,
human kinematics, it is currently unclear how to generalise convolutional
kernels in a principled manner. Therefore we define and implement here a
framework for general graph-structured CNNs for time series analysis. Our
algorithm automatically builds convolutional layers using the specified
adjacency matrix of the data dimensions and convolutional masks that scale with
the hop distance. In the limit of a lattice-topology our method produces the
well-known image convolutional masks. We test our method first on synthetic
data of arbitrarily-connected graphs and human hand motion capture data, where
the hand is represented by a tree capturing the mechanical dependencies of the
joints. We are able to demonstrate, amongst other things, that inclusion of the
graph structure of the data dimensions improves model prediction significantly,
when compared against a benchmark CNN model with only time convolution layers.
| null |
http://arxiv.org/abs/1803.05419v2
|
http://arxiv.org/pdf/1803.05419v2.pdf
| null |
[
"Thomas Teh",
"Chaiyawan Auepanwiriyakul",
"John Alexander Harston",
"A. Aldo Faisal"
] |
[
"Time Series",
"Time Series Analysis"
] | 2018-03-14T00:00:00 | 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/model-driven-artificial-intelligence-for
|
1805.12090
| null | null |
Problem-Adapted Artificial Intelligence for Online Network Optimization
|
Future 5G wireless networks will rely on agile and automated network
management, where the usage of diverse resources must be jointly optimized with
surgical accuracy. A number of key wireless network functionalities (e.g.,
traffic steering, power control) give rise to hard optimization problems. What
is more, high spatio-temporal traffic variability coupled with the need to
satisfy strict per slice/service SLAs in modern networks, suggest that these
problems must be constantly (re-)solved, to maintain close-to-optimal
performance. To this end, we propose the framework of Online Network
Optimization (ONO), which seeks to maintain both agile and efficient control
over time, using an arsenal of data-driven, online learning, and AI-based
techniques. Since the mathematical tools and the studied regimes vary widely
among these methodologies, a theoretical comparison is often out of reach.
Therefore, the important question `what is the right ONO technique?' remains
open to date. In this paper, we discuss the pros and cons of each technique and
present a direct quantitative comparison for a specific use case, using real
data. Our results suggest that carefully combining the insights of problem
modeling with state-of-the-art AI techniques provides significant advantages at
reasonable complexity.
| null |
http://arxiv.org/abs/1805.12090v2
|
http://arxiv.org/pdf/1805.12090v2.pdf
| null |
[
"Spyridon Vassilaras",
"Luigi Vigneri",
"Nikolaos Liakopoulos",
"Georgios S. Paschos",
"Apostolos Destounis",
"Thrasyvoulos Spyropoulos",
"Merouane Debbah"
] |
[
"Management"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mpdcompress-matrix-permutation-decomposition
|
1805.12085
| null | null |
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression
|
Deep neural networks (DNNs) have become the state-of-the-art technique for
machine learning tasks in various applications. However, due to their size and
the computational complexity, large DNNs are not readily deployable on edge
devices in real-time. To manage complexity and accelerate computation, network
compression techniques based on pruning and quantization have been proposed and
shown to be effective in reducing network size. However, such network
compression can result in irregular matrix structures that are mismatched with
modern hardware-accelerated platforms, such as graphics processing units (GPUs)
designed to perform the DNN matrix multiplications in a structured
(block-based) way. We propose MPDCompress, a DNN compression algorithm based on
matrix permutation decomposition via random mask generation. In-training
application of the masks molds the synaptic weight connection matrix to a
sub-graph separation format. Aided by the random permutations, a
hardware-desirable block matrix is generated, allowing for a more efficient
implementation and compression of the network. To show versatility, we
empirically verify MPDCompress on several network models, compression rates,
and image datasets. On the LeNet 300-100 model (MNIST dataset), Deep MNIST, and
CIFAR10, we achieve 10 X network compression with less than 1% accuracy loss
compared to non-compressed accuracy performance. On AlexNet for the full
ImageNet ILSVRC-2012 dataset, we achieve 8 X network compression with less than
1% accuracy loss, with top-5 and top-1 accuracies of 79.6% and 56.4%,
respectively. Finally, we observe that the algorithm can offer inference
speedups across various hardware platforms, with 4 X faster operation achieved
on several mobile GPUs.
| null |
http://arxiv.org/abs/1805.12085v1
|
http://arxiv.org/pdf/1805.12085v1.pdf
| null |
[
"Lazar Supic",
"Rawan Naous",
"Ranko Sredojevic",
"Aleksandra Faust",
"Vladimir Stojanovic"
] |
[
"Neural Network Compression",
"Quantization"
] | 2018-05-30T00:00:00 | null | null | null | 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": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"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": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"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/1c5c289b6218eb1026dcb5fd9738231401cfccea/torch/nn/modules/normalization.py#L13",
"description": "**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.\r\n\r\n$$ b_{c} = a_{c}\\left(k + \\frac{\\alpha}{n}\\sum_{c'=\\max(0, c-n/2)}^{\\min(N-1,c+n/2)}a_{c'}^2\\right)^{-\\beta} $$\r\n\r\nWhere the size is the number of neighbouring channels used for normalization, $\\alpha$ is multiplicative factor, $\\beta$ an exponent and $k$ an additive factor",
"full_name": "Local Response 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": "Local Response Normalization",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42",
"description": "A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://paperswithcode.com/method/alexnet) was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as [ResNeXt](https://paperswithcode.com/method/resnext), it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, *cardinality* (the size of set of transformations), we can increase accuracy by increasing it.",
"full_name": "Grouped Convolution",
"introduced_year": 2000,
"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": "Grouped Convolution",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"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": "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": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"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": "https://github.com/Elman295/Paper_with_code/blob/main/LeNet_5_Pytorch.ipynb",
"description": "**LeNet** is a classic convolutional neural network employing the use of convolutions, pooling and fully connected layers. It was used for the handwritten digit recognition task with the MNIST dataset. The architectural design served as inspiration for future networks such as [AlexNet](https://paperswithcode.com/method/alexnet) and [VGG](https://paperswithcode.com/method/vgg)..\r\n\r\n[code](https://github.com/Elman295/Paper_with_code/blob/main/LeNet_5_Pytorch.ipynb)",
"full_name": "LeNet",
"introduced_year": 1998,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "LeNet",
"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/dansuh17/alexnet-pytorch/blob/d0c1b1c52296ffcbecfbf5b17e1d1685b4ca6744/model.py#L40",
"description": "To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.ggfdf\r\n\r\n\r\nHow do I speak to a person at Expedia?How do I speak to a person at Expedia?To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.\r\n\r\n\r\n\r\nTo make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.chgd",
"full_name": "How do I speak to a person at Expedia?-/+/",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "How do I speak to a person at Expedia?-/+/",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
}
] |
https://paperswithcode.com/paper/towards-understanding-the-role-of-over
|
1805.12076
| null | null |
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks
|
Despite existing work on ensuring generalization of neural networks in terms
of scale sensitive complexity measures, such as norms, margin and sharpness,
these complexity measures do not offer an explanation of why neural networks
generalize better with over-parametrization. In this work we suggest a novel
complexity measure based on unit-wise capacities resulting in a tighter
generalization bound for two layer ReLU networks. Our capacity bound correlates
with the behavior of test error with increasing network sizes, and could
potentially explain the improvement in generalization with
over-parametrization. We further present a matching lower bound for the
Rademacher complexity that improves over previous capacity lower bounds for
neural networks.
|
Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization.
|
http://arxiv.org/abs/1805.12076v1
|
http://arxiv.org/pdf/1805.12076v1.pdf
| null |
[
"Behnam Neyshabur",
"Zhiyuan Li",
"Srinadh Bhojanapalli",
"Yann Lecun",
"Nathan Srebro"
] |
[] | 2018-05-30T00:00:00 | 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/fourier-policy-gradients
|
1802.06891
| null | null |
Fourier Policy Gradients
|
We propose a new way of deriving policy gradient updates for reinforcement
learning. Our technique, based on Fourier analysis, recasts integrals that
arise with expected policy gradients as convolutions and turns them into
multiplications. The obtained analytical solutions allow us to capture the low
variance benefits of EPG in a broad range of settings. For the critic, we treat
trigonometric and radial basis functions, two function families with the
universal approximation property. The choice of policy can be almost arbitrary,
including mixtures or hybrid continuous-discrete probability distributions.
Moreover, we derive a general family of sample-based estimators for stochastic
policy gradients, which unifies existing results on sample-based approximation.
We believe that this technique has the potential to shape the next generation
of policy gradient approaches, powered by analytical results.
| null |
http://arxiv.org/abs/1802.06891v2
|
http://arxiv.org/pdf/1802.06891v2.pdf
|
ICML 2018 7
|
[
"Matthew Fellows",
"Kamil Ciosek",
"Shimon Whiteson"
] |
[
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-02-19T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2414
|
http://proceedings.mlr.press/v80/fellows18a/fellows18a.pdf
|
fourier-policy-gradients-1
| null |
[] |
https://paperswithcode.com/paper/automatic-fast-and-robust-characterization-of
|
1805.12071
| null | null |
Automatic, fast and robust characterization of noise distributions for diffusion MRI
|
Knowledge of the noise distribution in magnitude diffusion MRI images is the
centerpiece to quantify uncertainties arising from the acquisition process. The
use of parallel imaging methods, the number of receiver coils and imaging
filters applied by the scanner, amongst other factors, dictate the resulting
signal distribution. Accurate estimation beyond textbook Rician or noncentral
chi distributions often requires information about the acquisition process
(e.g. coils sensitivity maps or reconstruction coefficients), which is not
usually available. We introduce a new method where a change of variable
naturally gives rise to a particular form of the gamma distribution for
background signals. The first moments and maximum likelihood estimators of this
gamma distribution explicitly depend on the number of coils, making it possible
to estimate all unknown parameters using only the magnitude data. A rejection
step is used to make the method automatic and robust to artifacts. Experiments
on synthetic datasets show that the proposed method can reliably estimate both
the degrees of freedom and the standard deviation. The worst case errors range
from below 2% (spatially uniform noise) to approximately 10% (spatially
variable noise). Repeated acquisitions of in vivo datasets show that the
estimated parameters are stable and have lower variances than compared methods.
|
Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process.
|
http://arxiv.org/abs/1805.12071v2
|
http://arxiv.org/pdf/1805.12071v2.pdf
| null |
[
"Samuel St-Jean",
"Alberto De Luca",
"Max A. Viergever",
"Alexander Leemans"
] |
[
"Diffusion MRI",
"Noise Estimation"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/code-switching-language-modeling-using-syntax
|
1805.12070
| null | null |
Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
|
Lack of text data has been the major issue on code-switching language
modeling. In this paper, we introduce multi-task learning based language model
which shares syntax representation of languages to leverage linguistic
information and tackle the low resource data issue. Our model jointly learns
both language modeling and Part-of-Speech tagging on code-switched utterances.
In this way, the model is able to identify the location of code-switching
points and improves the prediction of next word. Our approach outperforms
standard LSTM based language model, with an improvement of 9.7% and 7.4% in
perplexity on SEAME Phase I and Phase II dataset respectively.
| null |
http://arxiv.org/abs/1805.12070v2
|
http://arxiv.org/pdf/1805.12070v2.pdf
|
WS 2018 7
|
[
"Genta Indra Winata",
"Andrea Madotto",
"Chien-Sheng Wu",
"Pascale Fung"
] |
[
"Language Modeling",
"Language Modelling",
"Multi-Task Learning",
"Part-Of-Speech Tagging",
"Syntax Representation"
] | 2018-05-30T00:00:00 |
https://aclanthology.org/W18-3207
|
https://aclanthology.org/W18-3207.pdf
|
code-switching-language-modeling-using-syntax-1
| 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/retainvis-visual-analytics-with-interpretable
|
1805.10724
| null | null |
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
|
We have recently seen many successful applications of recurrent neural
networks (RNNs) on electronic medical records (EMRs), which contain histories
of patients' diagnoses, medications, and other various events, in order to
predict the current and future states of patients. Despite the strong
performance of RNNs, it is often challenging for users to understand why the
model makes a particular prediction. Such black-box nature of RNNs can impede
its wide adoption in clinical practice. Furthermore, we have no established
methods to interactively leverage users' domain expertise and prior knowledge
as inputs for steering the model. Therefore, our design study aims to provide a
visual analytics solution to increase interpretability and interactivity of
RNNs via a joint effort of medical experts, artificial intelligence scientists,
and visual analytics researchers. Following the iterative design process
between the experts, we design, implement, and evaluate a visual analytics tool
called RetainVis, which couples a newly improved, interpretable and interactive
RNN-based model called RetainEX and visualizations for users' exploration of
EMR data in the context of prediction tasks. Our study shows the effective use
of RetainVis for gaining insights into how individual medical codes contribute
to making risk predictions, using EMRs of patients with heart failure and
cataract symptoms. Our study also demonstrates how we made substantial changes
to the state-of-the-art RNN model called RETAIN in order to make use of
temporal information and increase interactivity. This study will provide a
useful guideline for researchers that aim to design an interpretable and
interactive visual analytics tool for RNNs.
| null |
http://arxiv.org/abs/1805.10724v3
|
http://arxiv.org/pdf/1805.10724v3.pdf
| null |
[
"Bum Chul Kwon",
"Min-Je Choi",
"Joanne Taery Kim",
"Edward Choi",
"Young Bin Kim",
"Soonwook Kwon",
"Jimeng Sun",
"Jaegul Choo"
] |
[] | 2018-05-28T00:00:00 | 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-robust-and-effective-approach-towards
|
1805.12067
| null | null |
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
|
Predicting TNM stage is the major determinant of breast cancer prognosis and
treatment. The essential part of TNM stage classification is whether the cancer
has metastasized to the regional lymph nodes (N-stage). Pathologic N-stage
(pN-stage) is commonly performed by pathologists detecting metastasis in
histological slides. However, this diagnostic procedure is prone to
misinterpretation and would normally require extensive time by pathologists
because of the sheer volume of data that needs a thorough review. Automated
detection of lymph node metastasis and pN-stage prediction has a great
potential to reduce their workload and help the pathologist. Recent advances in
convolutional neural networks (CNN) have shown significant improvements in
histological slide analysis, but accuracy is not optimized because of the
difficulty in the handling of gigapixel images. In this paper, we propose a
robust method for metastasis detection and pN-stage classification in breast
cancer from multiple gigapixel pathology images in an effective way. pN-stage
is predicted by combining patch-level CNN based metastasis detector and
slide-level lymph node classifier. The proposed framework achieves a
state-of-the-art quadratic weighted kappa score of 0.9203 on the Camelyon17
dataset, outperforming the previous winning method of the Camelyon17 challenge.
| null |
http://arxiv.org/abs/1805.12067v1
|
http://arxiv.org/pdf/1805.12067v1.pdf
| null |
[
"Byungjae Lee",
"Kyunghyun Paeng"
] |
[
"Diagnostic",
"General Classification",
"Prognosis"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/human-vs-automatic-metrics-on-the-importance
|
1805.11474
| null | null |
Human vs Automatic Metrics: on the Importance of Correlation Design
|
This paper discusses two existing approaches to the correlation analysis between automatic evaluation metrics and human scores in the area of natural language generation. Our experiments show that depending on the usage of a system- or sentence-level correlation analysis, correlation results between automatic scores and human judgments are inconsistent.
|
This paper discusses two existing approaches to the correlation analysis between automatic evaluation metrics and human scores in the area of natural language generation.
|
https://arxiv.org/abs/1805.11474v3
|
https://arxiv.org/pdf/1805.11474v3.pdf
| null |
[
"Anastasia Shimorina"
] |
[
"Sentence",
"Text Generation"
] | 2018-05-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stochastic-deep-compressive-sensing-for-the
|
1805.12064
| null | null |
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
|
Understanding the structure of the heart at the microscopic scale of
cardiomyocytes and their aggregates provides new insights into the mechanisms
of heart disease and enables the investigation of effective therapeutics.
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive
technique that can resolve the microscopic structure, organisation, and
integrity of the myocardium without the need for exogenous contrast agents.
However, this technique suffers from relatively low signal-to-noise ratio (SNR)
and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR
techniques rely on acquiring and averaging multiple signal acquisitions to
improve the SNR. Moreover, in order to mitigate the influence of respiratory
movement, patients are required to perform many breath holds which results in
prolonged acquisition durations (e.g., ~30 mins using the existing technology).
In this study, we propose a novel cascaded Convolutional Neural Networks (CNN)
based compressive sensing (CS) technique and explore its applicability to
improve DT-CMR acquisitions. Our simulation based studies have achieved high
reconstruction fidelity and good agreement between DT-CMR parameters obtained
with the proposed reconstruction and fully sampled ground truth. When compared
to other state-of-the-art methods, our proposed deep cascaded CNN method and
its stochastic variation demonstrated significant improvements. To the best of
our knowledge, this is the first study using deep CNN based CS for the DT-CMR
reconstruction. In addition, with relatively straightforward modifications to
the acquisition scheme, our method can easily be translated into a method for
online, at-the-scanner reconstruction enabling the deployment of accelerated
DT-CMR in various clinical applications.
| null |
http://arxiv.org/abs/1805.12064v1
|
http://arxiv.org/pdf/1805.12064v1.pdf
| null |
[
"Jo Schlemper",
"Guang Yang",
"Pedro Ferreira",
"Andrew Scott",
"Laura-Ann McGill",
"Zohya Khalique",
"Margarita Gorodezky",
"Malte Roehl",
"Jennifer Keegan",
"Dudley Pennell",
"David Firmin",
"Daniel Rueckert"
] |
[
"Compressive Sensing"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/regularized-kernel-and-neural-sobolev-descent
|
1805.12062
| null | null |
Sobolev Descent
|
We study a simplification of GAN training: the problem of transporting particles from a source to a target distribution. Starting from the Sobolev GAN critic, part of the gradient regularized GAN family, we show a strong relation with Optimal Transport (OT). Specifically with the less popular dynamic formulation of OT that finds a path of distributions from source to target minimizing a ``kinetic energy''. We introduce Sobolev descent that constructs similar paths by following gradient flows of a critic function in a kernel space or parametrized by a neural network. In the kernel version, we show convergence to the target distribution in the MMD sense. We show in theory and experiments that regularization has an important role in favoring smooth transitions between distributions, avoiding large gradients from the critic. This analysis in a simplified particle setting provides insight in paths to equilibrium in GANs.
| null |
https://arxiv.org/abs/1805.12062v2
|
https://arxiv.org/pdf/1805.12062v2.pdf
| null |
[
"Youssef Mroueh",
"Tom Sercu",
"Anant Raj"
] |
[] | 2018-05-30T00:00:00 | 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/bilingual-character-representation-for
|
1805.12061
| null | null |
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition
|
We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data. Our model uses bilingual character representation and transfer learning to address out-of-vocabulary words. In order to mitigate data noise, we propose to use token replacement and normalization. In the 3rd Workshop on Computational Approaches to Linguistic Code-Switching Shared Task, we achieved second place with 62.76% harmonic mean F1-score for English-Spanish language pair without using any gazetteer and knowledge-based information.
| null |
https://arxiv.org/abs/1805.12061v2
|
https://arxiv.org/pdf/1805.12061v2.pdf
|
WS 2018 7
|
[
"Genta Indra Winata",
"Chien-Sheng Wu",
"Andrea Madotto",
"Pascale Fung"
] |
[
"named-entity-recognition",
"Named Entity Recognition",
"Named Entity Recognition (NER)",
"Transfer Learning"
] | 2018-05-30T00:00:00 |
https://aclanthology.org/W18-3214
|
https://aclanthology.org/W18-3214.pdf
|
bilingual-character-representation-for-1
| null |
[] |
https://paperswithcode.com/paper/im2flow-motion-hallucination-from-static
|
1712.04109
| null | null |
Im2Flow: Motion Hallucination from Static Images for Action Recognition
|
Existing methods to recognize actions in static images take the images at
their face value, learning the appearances---objects, scenes, and body
poses---that distinguish each action class. However, such models are deprived
of the rich dynamic structure and motions that also define human activity. We
propose an approach that hallucinates the unobserved future motion implied by a
single snapshot to help static-image action recognition. The key idea is to
learn a prior over short-term dynamics from thousands of unlabeled videos,
infer the anticipated optical flow on novel static images, and then train
discriminative models that exploit both streams of information. Our main
contributions are twofold. First, we devise an encoder-decoder convolutional
neural network and a novel optical flow encoding that can translate a static
image into an accurate flow map. Second, we show the power of hallucinated flow
for recognition, successfully transferring the learned motion into a standard
two-stream network for activity recognition. On seven datasets, we demonstrate
the power of the approach. It not only achieves state-of-the-art accuracy for
dense optical flow prediction, but also consistently enhances recognition of
actions and dynamic scenes.
|
Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition.
|
http://arxiv.org/abs/1712.04109v3
|
http://arxiv.org/pdf/1712.04109v3.pdf
|
CVPR 2018 6
|
[
"Ruohan Gao",
"Bo Xiong",
"Kristen Grauman"
] |
[
"Action Recognition",
"Activity Recognition",
"Decoder",
"Hallucination",
"Optical Flow Estimation",
"Temporal Action Localization"
] | 2017-12-12T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Gao_Im2Flow_Motion_Hallucination_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Gao_Im2Flow_Motion_Hallucination_CVPR_2018_paper.pdf
|
im2flow-motion-hallucination-from-static-1
| null |
[] |
https://paperswithcode.com/paper/semi-supervised-phone-classification-using
|
1612.04899
| null | null |
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization
|
We describe a graph-based semi-supervised learning framework in the context
of deep neural networks that uses a graph-based entropic regularizer to favor
smooth solutions over a graph induced by the data. The main contribution of
this work is a computationally efficient, stochastic graph-regularization
technique that uses mini-batches that are consistent with the graph structure,
but also provides enough stochasticity (in terms of mini-batch data diversity)
for convergence of stochastic gradient descent methods to good solutions. For
this work, we focus on results of frame-level phone classification accuracy on
the TIMIT speech corpus but our method is general and scalable to much larger
data sets. Results indicate that our method significantly improves
classification accuracy compared to the fully-supervised case when the fraction
of labeled data is low, and it is competitive with other methods in the fully
labeled case.
| null |
http://arxiv.org/abs/1612.04899v2
|
http://arxiv.org/pdf/1612.04899v2.pdf
| null |
[
"Sunil Thulasidasan",
"Jeffrey Bilmes"
] |
[
"Diversity",
"General Classification"
] | 2016-12-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robust-place-categorization-with-deep-domain
|
1805.12048
| null | null |
Robust Place Categorization with Deep Domain Generalization
|
Traditional place categorization approaches in robot vision assume that
training and test images have similar visual appearance. Therefore, any
seasonal, illumination and environmental changes typically lead to severe
degradation in performance. To cope with this problem, recent works have
proposed to adopt domain adaptation techniques. While effective, these methods
assume that some prior information about the scenario where the robot will
operate is available at training time. Unfortunately, in many cases this
assumption does not hold, as we often do not know where a robot will be
deployed. To overcome this issue, in this paper we present an approach which
aims at learning classification models able to generalize to unseen scenarios.
Specifically, we propose a novel deep learning framework for domain
generalization. Our method develops from the intuition that, given a set of
different classification models associated to known domains (e.g. corresponding
to multiple environments, robots), the best model for a new sample in the novel
domain can be computed directly at test time by optimally combining the known
models. To implement our idea, we exploit recent advances in deep domain
adaptation and design a Convolutional Neural Network architecture with novel
layers performing a weighted version of Batch Normalization. Our experiments,
conducted on three common datasets for robot place categorization, confirm the
validity of our contribution.
|
Our method develops from the intuition that, given a set of different classification models associated to known domains (e. g. corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models.
|
http://arxiv.org/abs/1805.12048v1
|
http://arxiv.org/pdf/1805.12048v1.pdf
| null |
[
"Massimiliano Mancini",
"Samuel Rota Bulò",
"Barbara Caputo",
"Elisa Ricci"
] |
[
"Domain Adaptation",
"Domain Generalization",
"General Classification"
] | 2018-05-30T00:00:00 | null | null | null | null |
[
{
"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"
}
] |
https://paperswithcode.com/paper/classifying-cooking-objects-state-using-a
|
1805.09391
| null | null |
Classifying cooking object's state using a tuned VGG convolutional neural network
|
In robotics, knowing the object states and recognizing the desired states are
very important. Objects at different states would require different grasping.
To achieve different states, different manipulations would be required, as well
as different grasping. To analyze the objects at different states, a dataset of
cooking objects was created. Cooking consists of various cutting techniques
needed for different dishes (e.g. diced, julienne etc.). Identifying each of
this state of cooking objects by the human can be difficult sometimes too. In
this paper, we have analyzed seven different cooking object states by tuning a
convolutional neural network (CNN). For this task, images were downloaded and
annotated by students and they are divided into training and a completely
different test set. By tuning the vgg-16 CNN 77% accuracy was obtained. The
work presented in this paper focuses on classification between various object
states rather than task recognition or recipe prediction. This framework can be
easily adapted in any other object state classification activity.
| null |
http://arxiv.org/abs/1805.09391v2
|
http://arxiv.org/pdf/1805.09391v2.pdf
| null |
[
"Rahul Paul"
] |
[
"General Classification",
"Object"
] | 2018-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/end-to-end-named-entity-extraction-from
|
1805.12045
| null | null |
End-to-end named entity extraction from speech
|
Named entity recognition (NER) is among SLU tasks that usually extract
semantic information from textual documents. Until now, NER from speech is made
through a pipeline process that consists in processing first an automatic
speech recognition (ASR) on the audio and then processing a NER on the ASR
outputs. Such approach has some disadvantages (error propagation, metric to
tune ASR systems sub-optimal in regards to the final task, reduced space search
at the ASR output level...) and it is known that more integrated approaches
outperform sequential ones, when they can be applied. In this paper, we present
a first study of end-to-end approach that directly extracts named entities from
speech, though a unique neural architecture. On a such way, a joint
optimization is able for both ASR and NER. Experiments are carried on French
data easily accessible, composed of data distributed in several evaluation
campaign. Experimental results show that this end-to-end approach provides
better results (F-measure=0.69 on test data) than a classical pipeline approach
to detect named entity categories (F-measure=0.65).
| null |
http://arxiv.org/abs/1805.12045v1
|
http://arxiv.org/pdf/1805.12045v1.pdf
| null |
[
"Sahar Ghannay",
"Antoine Caubrière",
"Yannick Estève",
"Antoine Laurent",
"Emmanuel Morin"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"Entity Extraction using GAN",
"named-entity-recognition",
"Named Entity Recognition",
"Named Entity Recognition (NER)",
"NER",
"speech-recognition",
"Speech Recognition"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bi-directional-neural-machine-translation
|
1805.11213
| null | null |
Bi-Directional Neural Machine Translation with Synthetic Parallel Data
|
Despite impressive progress in high-resource settings, Neural Machine
Translation (NMT) still struggles in low-resource and out-of-domain scenarios,
often failing to match the quality of phrase-based translation. We propose a
novel technique that combines back-translation and multilingual NMT to improve
performance in these difficult cases. Our technique trains a single model for
both directions of a language pair, allowing us to back-translate source or
target monolingual data without requiring an auxiliary model. We then continue
training on the augmented parallel data, enabling a cycle of improvement for a
single model that can incorporate any source, target, or parallel data to
improve both translation directions. As a byproduct, these models can reduce
training and deployment costs significantly compared to uni-directional models.
Extensive experiments show that our technique outperforms standard
back-translation in low-resource scenarios, improves quality on cross-domain
tasks, and effectively reduces costs across the board.
| null |
http://arxiv.org/abs/1805.11213v2
|
http://arxiv.org/pdf/1805.11213v2.pdf
|
WS 2018 7
|
[
"Xing Niu",
"Michael Denkowski",
"Marine Carpuat"
] |
[
"Machine Translation",
"NMT",
"Translation"
] | 2018-05-29T00:00:00 |
https://aclanthology.org/W18-2710
|
https://aclanthology.org/W18-2710.pdf
|
bi-directional-neural-machine-translation-1
| null |
[] |
https://paperswithcode.com/paper/predicting-county-level-corn-yields-using
|
1805.12044
| null | null |
Predicting County Level Corn Yields Using Deep Long Short Term Memory Models
|
Corn yield prediction is beneficial as it provides valuable information about
production and prices prior the harvest. Publicly available high-quality corn
yield prediction can help address emergent information asymmetry problems and
in doing so improve price efficiency in futures markets. This paper is the
first to employ Long Short-Term Memory (LSTM), a special form of Recurrent
Neural Network (RNN) method to predict corn yields. A cross sectional time
series of county-level corn yield and hourly weather data made the sample space
large enough to use deep learning technics. LSTM is efficient in time series
prediction with complex inner relations, which makes it suitable for this task.
The empirical results from county level data in Iowa show promising predictive
power relative to existing survey based methods.
| null |
http://arxiv.org/abs/1805.12044v1
|
http://arxiv.org/pdf/1805.12044v1.pdf
| null |
[
"Zehui Jiang",
"Chao Liu",
"Nathan P. Hendricks",
"Baskar Ganapathysubramanian",
"Dermot J. Hayes",
"Soumik Sarkar"
] |
[
"Prediction",
"Time Series",
"Time Series Analysis",
"Time Series Prediction"
] | 2018-05-30T00:00:00 | 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/adaptive-system-identification-using-lms
|
1806.01782
| null | null |
Adaptive System Identification Using LMS Algorithm Integrated with Evolutionary Computation
|
System identification is an exceptionally expansive topic and of remarkable
significance in the discipline of signal processing and communication. Our goal
in this paper is to show how simple adaptive FIR and IIR filters can be used in
system modeling and demonstrating the application of adaptive system
identification. The main objective of our research is to study the LMS
algorithm and its improvement by the genetic search approach, namely, LMS-GA,
to search the multi-modal error surface of the IIR filter to avoid local minima
and finding the optimal weight vector when only measured or estimated data are
available. Convergence analysis of the LMS algorithm in the case of coloured
input signal, i.e., correlated input signal is demonstrated on adaptive FIR
filter via power spectral density of the input signals and Fourier transform of
the autocorrelation matrix of the input signal. Simulations have been carried
out on adaptive filtering of FIR and IIR filters and tested on white and
coloured input signals to validate the powerfulness of the genetic-based LMS
algorithm.
| null |
http://arxiv.org/abs/1806.01782v2
|
http://arxiv.org/pdf/1806.01782v2.pdf
| null |
[
"Ibraheem Kasim Ibraheem"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/identifying-and-understanding-user-reactions
|
1805.12032
| null | null |
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
|
In the age of social news, it is important to understand the types of
reactions that are evoked from news sources with various levels of credibility.
In the present work we seek to better understand how users react to trusted and
deceptive news sources across two popular, and very different, social media
platforms. To that end, (1) we develop a model to classify user reactions into
one of nine types, such as answer, elaboration, and question, etc, and (2) we
measure the speed and the type of reaction for trusted and deceptive news
sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there
are significant differences in the speed and the type of reactions between
trusted and deceptive news sources on Twitter, but far smaller differences on
Reddit.
| null |
http://arxiv.org/abs/1805.12032v1
|
http://arxiv.org/pdf/1805.12032v1.pdf
|
ACL 2018 7
|
[
"Maria Glenski",
"Tim Weninger",
"Svitlana Volkova"
] |
[] | 2018-05-30T00:00:00 |
https://aclanthology.org/P18-2029
|
https://aclanthology.org/P18-2029.pdf
|
identifying-and-understanding-user-reactions-1
| 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/a-contextual-bandit-bake-off
|
1802.04064
| null | null |
A Contextual Bandit Bake-off
|
Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally efficient methods, the practical behavior of these algorithms is still poorly understood. We leverage the availability of large numbers of supervised learning datasets to empirically evaluate contextual bandit algorithms, focusing on practical methods that learn by relying on optimization oracles from supervised learning. We find that a recent method (Foster et al., 2018) using optimism under uncertainty works the best overall. A surprisingly close second is a simple greedy baseline that only explores implicitly through the diversity of contexts, followed by a variant of Online Cover (Agarwal et al., 2014) which tends to be more conservative but robust to problem specification by design. Along the way, we also evaluate various components of contextual bandit algorithm design such as loss estimators. Overall, this is a thorough study and review of contextual bandit methodology.
|
Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems.
|
https://arxiv.org/abs/1802.04064v4
|
https://arxiv.org/pdf/1802.04064v4.pdf
| null |
[
"Alberto Bietti",
"Alekh Agarwal",
"John Langford"
] |
[
"Diversity"
] | 2018-02-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/privacy-aware-offloading-of-deep-neural
|
1805.12024
| null | null |
Privacy Aware Offloading of Deep Neural Networks
|
Deep neural networks require large amounts of resources which makes them hard
to use on resource constrained devices such as Internet-of-things devices.
Offloading the computations to the cloud can circumvent these constraints but
introduces a privacy risk since the operator of the cloud is not necessarily
trustworthy. We propose a technique that obfuscates the data before sending it
to the remote computation node. The obfuscated data is unintelligible for a
human eavesdropper but can still be classified with a high accuracy by a neural
network trained on unobfuscated images.
| null |
http://arxiv.org/abs/1805.12024v1
|
http://arxiv.org/pdf/1805.12024v1.pdf
| null |
[
"Sam Leroux",
"Tim Verbelen",
"Pieter Simoens",
"Bart Dhoedt"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/towards-adversarial-configurations-for
|
1805.12021
| null | null |
Towards Adversarial Configurations for Software Product Lines
|
Ensuring that all supposedly valid configurations of a software product line
(SPL) lead to well-formed and acceptable products is challenging since it is
most of the time impractical to enumerate and test all individual products of
an SPL. Machine learning classifiers have been recently used to predict the
acceptability of products associated with unseen configurations. For some
configurations, a tiny change in their feature values can make them pass from
acceptable to non-acceptable regarding users' requirements and vice-versa. In
this paper, we introduce the idea of leveraging these specific configurations
and their positions in the feature space to improve the classifier and
therefore the engineering of an SPL. Starting from a variability model, we
propose to use Adversarial Machine Learning techniques to create new,
adversarial configurations out of already known configurations by modifying
their feature values. Using an industrial video generator we show how
adversarial configurations can improve not only the classifier, but also the
variability model, the variability implementation, and the testing oracle.
| null |
http://arxiv.org/abs/1805.12021v1
|
http://arxiv.org/pdf/1805.12021v1.pdf
| null |
[
"Paul Temple",
"Mathieu Acher",
"Battista Biggio",
"Jean-Marc Jézéquel",
"Fabio Roli"
] |
[
"BIG-bench Machine Learning",
"valid"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generalizing-to-unseen-domains-via
|
1805.12018
| null | null |
Generalizing to Unseen Domains via Adversarial Data Augmentation
|
We are concerned with learning models that generalize well to different
\emph{unseen} domains. We consider a worst-case formulation over data
distributions that are near the source domain in the feature space. Only using
training data from a single source distribution, we propose an iterative
procedure that augments the dataset with examples from a fictitious target
domain that is "hard" under the current model. We show that our iterative
scheme is an adaptive data augmentation method where we append adversarial
examples at each iteration. For softmax losses, we show that our method is a
data-dependent regularization scheme that behaves differently from classical
regularizers that regularize towards zero (e.g., ridge or lasso). On digit
recognition and semantic segmentation tasks, our method learns models improve
performance across a range of a priori unknown target domains.
|
Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model.
|
http://arxiv.org/abs/1805.12018v2
|
http://arxiv.org/pdf/1805.12018v2.pdf
|
NeurIPS 2018 12
|
[
"Riccardo Volpi",
"Hongseok Namkoong",
"Ozan Sener",
"John Duchi",
"Vittorio Murino",
"Silvio Savarese"
] |
[
"Data Augmentation",
"Semantic Segmentation"
] | 2018-05-30T00:00:00 |
http://papers.nips.cc/paper/7779-generalizing-to-unseen-domains-via-adversarial-data-augmentation
|
http://papers.nips.cc/paper/7779-generalizing-to-unseen-domains-via-adversarial-data-augmentation.pdf
|
generalizing-to-unseen-domains-via-1
| 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/counterstrike-defending-deep-learning
|
1805.12017
| null | null |
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
|
Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a lot of defense methods were proposed, which however, have been circumvented by newer attacking strategies. In the midst of this ensuing arms race, the problem of robustness against adversarial attacks still remains unsolved. This paper proposes a novel, simple yet effective defense strategy where adversarial samples are relaxed onto the underlying manifold of the (unknown) target class distribution. Specifically, our algorithm drives off-manifold adversarial samples towards high density regions of the data generating distribution of the target class by the Metroplis-adjusted Langevin algorithm (MALA) with perceptual boundary taken into account. Although the motivation is similar to projection methods, e.g., Defense-GAN, our algorithm, called MALA for DEfense (MALADE), is equipped with significant dispersion - projection is distributed broadly, and therefore any whitebox attack cannot accurately align the input so that the MALADE moves it to a targeted untrained spot where the model predicts a wrong label. In our experiments, MALADE exhibited state-of-the-art performance against various elaborate attacking strategies.
| null |
https://arxiv.org/abs/1805.12017v2
|
https://arxiv.org/pdf/1805.12017v2.pdf
| null |
[
"Vignesh Srinivasan",
"Arturo Marban",
"Klaus-Robert Müller",
"Wojciech Samek",
"Shinichi Nakajima"
] |
[
"Denoising"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/xdeepfm-combining-explicit-and-implicit
|
1803.05170
| null | null |
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
|
Combinatorial features are essential for the success of many commercial
models. Manually crafting these features usually comes with high cost due to
the variety, volume and velocity of raw data in web-scale systems.
Factorization based models, which measure interactions in terms of vector
product, can learn patterns of combinatorial features automatically and
generalize to unseen features as well. With the great success of deep neural
networks (DNNs) in various fields, recently researchers have proposed several
DNN-based factorization model to learn both low- and high-order feature
interactions. Despite the powerful ability of learning an arbitrary function
from data, plain DNNs generate feature interactions implicitly and at the
bit-wise level. In this paper, we propose a novel Compressed Interaction
Network (CIN), which aims to generate feature interactions in an explicit
fashion and at the vector-wise level. We show that the CIN share some
functionalities with convolutional neural networks (CNNs) and recurrent neural
networks (RNNs). We further combine a CIN and a classical DNN into one unified
model, and named this new model eXtreme Deep Factorization Machine (xDeepFM).
On one hand, the xDeepFM is able to learn certain bounded-degree feature
interactions explicitly; on the other hand, it can learn arbitrary low- and
high-order feature interactions implicitly. We conduct comprehensive
experiments on three real-world datasets. Our results demonstrate that xDeepFM
outperforms state-of-the-art models. We have released the source code of
xDeepFM at \url{https://github.com/Leavingseason/xDeepFM}.
|
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
|
http://arxiv.org/abs/1803.05170v3
|
http://arxiv.org/pdf/1803.05170v3.pdf
| null |
[
"Jianxun Lian",
"Xiaohuan Zhou",
"Fuzheng Zhang",
"Zhongxia Chen",
"Xing Xie",
"Guangzhong Sun"
] |
[
"Click-Through Rate Prediction",
"Recommendation Systems"
] | 2018-03-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-discrete-representation-learning
|
1711.00937
| null | null |
Neural Discrete Representation Learning
|
Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network outputs discrete, rather than continuous, codes; and the
prior is learnt rather than static. In order to learn a discrete latent
representation, we incorporate ideas from vector quantisation (VQ). Using the
VQ method allows the model to circumvent issues of "posterior collapse" --
where the latents are ignored when they are paired with a powerful
autoregressive decoder -- typically observed in the VAE framework. Pairing
these representations with an autoregressive prior, the model can generate high
quality images, videos, and speech as well as doing high quality speaker
conversion and unsupervised learning of phonemes, providing further evidence of
the utility of the learnt representations.
|
Learning useful representations without supervision remains a key challenge in machine learning.
|
http://arxiv.org/abs/1711.00937v2
|
http://arxiv.org/pdf/1711.00937v2.pdf
|
NeurIPS 2017 12
|
[
"Aaron van den Oord",
"Oriol Vinyals",
"Koray Kavukcuoglu"
] |
[
"Decoder",
"Representation Learning"
] | 2017-11-02T00:00:00 |
http://papers.nips.cc/paper/7210-neural-discrete-representation-learning
|
http://papers.nips.cc/paper/7210-neural-discrete-representation-learning.pdf
|
neural-discrete-representation-learning-1
| 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": "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/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": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"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": "",
"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": "**VQ-VAE** is a type of variational autoencoder that uses vector quantisation to obtain a discrete latent representation. It differs from [VAEs](https://paperswithcode.com/method/vae) in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, ideas from vector quantisation (VQ) are incorporated. Using the VQ method allows the model to circumvent issues of posterior collapse - where the latents are ignored when they are paired with a powerful autoregressive decoder - typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes.",
"full_name": "VQ-VAE",
"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": "VQ-VAE",
"source_title": "Neural Discrete Representation Learning",
"source_url": "http://arxiv.org/abs/1711.00937v2"
}
] |
https://paperswithcode.com/paper/bit-fusion-bit-level-dynamically-composable
|
1712.01507
| null | null |
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
|
Fully realizing the potential of acceleration for Deep Neural Networks (DNNs)
requires understanding and leveraging algorithmic properties. This paper builds
upon the algorithmic insight that bitwidth of operations in DNNs can be reduced
without compromising their classification accuracy. However, to prevent
accuracy loss, the bitwidth varies significantly across DNNs and it may even be
adjusted for each layer. Thus, a fixed-bitwidth accelerator would either offer
limited benefits to accommodate the worst-case bitwidth requirements, or lead
to a degradation in final accuracy. To alleviate these deficiencies, this work
introduces dynamic bit-level fusion/decomposition as a new dimension in the
design of DNN accelerators. We explore this dimension by designing Bit Fusion,
a bit-flexible accelerator, that constitutes an array of bit-level processing
elements that dynamically fuse to match the bitwidth of individual DNN layers.
This flexibility in the architecture enables minimizing the computation and the
communication at the finest granularity possible with no loss in accuracy. We
evaluate the benefits of BitFusion using eight real-world feed-forward and
recurrent DNNs. The proposed microarchitecture is implemented in Verilog and
synthesized in 45 nm technology. Using the synthesis results and cycle accurate
simulation, we compare the benefits of Bit Fusion to two state-of-the-art DNN
accelerators, Eyeriss and Stripes. In the same area, frequency, and process
technology, BitFusion offers 3.9x speedup and 5.1x energy savings over Eyeriss.
Compared to Stripes, BitFusion provides 2.6x speedup and 3.9x energy reduction
at 45 nm node when BitFusion area and frequency are set to those of Stripes.
Scaling to GPU technology node of 16 nm, BitFusion almost matches the
performance of a 250-Watt Titan Xp, which uses 8-bit vector instructions, while
BitFusion merely consumes 895 milliwatts of power.
| null |
http://arxiv.org/abs/1712.01507v2
|
http://arxiv.org/pdf/1712.01507v2.pdf
| null |
[
"Hardik Sharma",
"Jongse Park",
"Naveen Suda",
"Liangzhen Lai",
"Benson Chau",
"Joon Kyung Kim",
"Vikas Chandra",
"Hadi Esmaeilzadeh"
] |
[
"GPU"
] | 2017-12-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/why-is-my-classifier-discriminatory
|
1805.12002
| null | null |
Why Is My Classifier Discriminatory?
|
Recent attempts to achieve fairness in predictive models focus on the balance
between fairness and accuracy. In sensitive applications such as healthcare or
criminal justice, this trade-off is often undesirable as any increase in
prediction error could have devastating consequences. In this work, we argue
that the fairness of predictions should be evaluated in context of the data,
and that unfairness induced by inadequate samples sizes or unmeasured
predictive variables should be addressed through data collection, rather than
by constraining the model. We decompose cost-based metrics of discrimination
into bias, variance, and noise, and propose actions aimed at estimating and
reducing each term. Finally, we perform case-studies on prediction of income,
mortality, and review ratings, confirming the value of this analysis. We find
that data collection is often a means to reduce discrimination without
sacrificing accuracy.
| null |
http://arxiv.org/abs/1805.12002v2
|
http://arxiv.org/pdf/1805.12002v2.pdf
|
NeurIPS 2018 12
|
[
"Irene Chen",
"Fredrik D. Johansson",
"David Sontag"
] |
[
"Fairness"
] | 2018-05-30T00:00:00 |
http://papers.nips.cc/paper/7613-why-is-my-classifier-discriminatory
|
http://papers.nips.cc/paper/7613-why-is-my-classifier-discriminatory.pdf
|
why-is-my-classifier-discriminatory-1
| null |
[] |
https://paperswithcode.com/paper/streamlined-deployment-for-quantized-neural
|
1709.04060
| null | null |
Streamlined Deployment for Quantized Neural Networks
|
Running Deep Neural Network (DNN) models on devices with limited
computational capability is a challenge due to large compute and memory
requirements. Quantized Neural Networks (QNNs) have emerged as a potential
solution to this problem, promising to offer most of the DNN accuracy benefits
with much lower computational cost. However, harvesting these benefits on
existing mobile CPUs is a challenge since operations on highly quantized
datatypes are not natively supported in most instruction set architectures
(ISAs). In this work, we first describe a streamlining flow to convert all QNN
inference operations to integer ones. Afterwards, we provide techniques based
on processing one bit position at a time (bit-serial) to show how QNNs can be
efficiently deployed using common bitwise operations. We demonstrate the
potential of QNNs on mobile CPUs with microbenchmarks and on a quantized
AlexNet, which is 3.5x faster than an optimized 8-bit baseline. Our bit-serial
matrix multiplication library is available on GitHub at https://git.io/vhshn
|
Quantized Neural Networks (QNNs) have emerged as a potential solution to this problem, promising to offer most of the DNN accuracy benefits with much lower computational cost.
|
http://arxiv.org/abs/1709.04060v2
|
http://arxiv.org/pdf/1709.04060v2.pdf
| null |
[
"Yaman Umuroglu",
"Magnus Jahre"
] |
[] | 2017-09-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/using-neural-generative-models-to-release
|
1606.01151
| null | null |
Using Neural Generative Models to Release Synthetic Twitter Corpora with Reduced Stylometric Identifiability of Users
|
We present a method for generating synthetic versions of Twitter data using
neural generative models. The goal is protecting individuals in the source data
from stylometric re-identification attacks while still releasing data that
carries research value. Specifically, we generate tweet corpora that maintain
user-level word distributions by augmenting the neural language models with
user-specific components. We compare our approach to two standard text data
protection methods: redaction and iterative translation. We evaluate the three
methods on measures of risk and utility. We define risk following the
stylometric models of re-identification, and we define utility based on two
general word distribution measures and two common text analysis research tasks.
We find that neural models are able to significantly lower risk over previous
methods with little cost to utility. We also demonstrate that the neural models
allow data providers to actively control the risk-utility trade-off through
model tuning parameters. This work presents promising results for a new tool
addressing the problem of privacy for free text and sharing social media data
in a way that respects privacy and is ethically responsible.
| null |
http://arxiv.org/abs/1606.01151v4
|
http://arxiv.org/pdf/1606.01151v4.pdf
| null |
[
"Alexander G. Ororbia II",
"Fridolin Linder",
"Joshua Snoke"
] |
[
"Translation"
] | 2016-06-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adaptive-network-sparsification-with
|
1805.10896
| null |
rylfl6VFDH
|
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout
|
While variational dropout approaches have been shown to be effective for
network sparsification, they are still suboptimal in the sense that they set
the dropout rate for each neuron without consideration of the input data. With
such input-independent dropout, each neuron is evolved to be generic across
inputs, which makes it difficult to sparsify networks without accuracy loss. To
overcome this limitation, we propose adaptive variational dropout whose
probabilities are drawn from sparsity-inducing beta Bernoulli prior. It allows
each neuron to be evolved either to be generic or specific for certain inputs,
or dropped altogether. Such input-adaptive sparsity-inducing dropout allows the
resulting network to tolerate larger degree of sparsity without losing its
expressive power by removing redundancies among features. We validate our
dependent variational beta-Bernoulli dropout on multiple public datasets, on
which it obtains significantly more compact networks than baseline methods,
with consistent accuracy improvements over the base networks.
|
With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss.
|
http://arxiv.org/abs/1805.10896v3
|
http://arxiv.org/pdf/1805.10896v3.pdf
| null |
[
"Juho Lee",
"Saehoon Kim",
"Jaehong Yoon",
"Hae Beom Lee",
"Eunho Yang",
"Sung Ju Hwang"
] |
[] | 2018-05-28T00:00:00 |
https://openreview.net/forum?id=rylfl6VFDH
|
https://openreview.net/pdf?id=rylfl6VFDH
| null | null |
[
{
"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": "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/l0-norm-based-centers-selection-for-training
|
1805.11987
| null | null |
l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers
|
The aim of this paper is to train an RBF neural network and select centers
under concurrent faults. It is well known that fault tolerance is a very
attractive property for neural networks. And center selection is an important
procedure during the training process of an RBF neural network. In this paper,
we devise two novel algorithms to address these two issues simultaneously. Both
of them are based on the ADMM framework. In the first method, the minimax
concave penalty (MCP) function is introduced to select centers. In the second
method, an l0-norm term is directly used, and the hard threshold (HT) is
utilized to address the l0-norm term. Under several mild conditions, we can
prove that both methods can globally converge to a unique limit point.
Simulation results show that, under concurrent fault, the proposed algorithms
are superior to many existing methods.
| null |
http://arxiv.org/abs/1805.11987v3
|
http://arxiv.org/pdf/1805.11987v3.pdf
| null |
[
"Hao Wang",
"Chi-Sing Leung",
"Hing Cheung So",
"Ruibin Feng",
"Zifa Han"
] |
[] | 2018-05-30T00:00:00 | 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/automatic-generation-of-object-shapes-with
|
1805.11984
| null |
B1x0enCcK7
|
Automatic generation of object shapes with desired functionalities
|
3D objects (artefacts) are made to fulfill functions. Designing an object
often starts with defining a list of functionalities that it should provide,
also known as functional requirements. Today, the design of 3D object models is
still a slow and largely artisanal activity, with few Computer-Aided Design
(CAD) tools existing to aid the exploration of the design solution space. To
accelerate the design process, we introduce an algorithm for generating object
shapes with desired functionalities. Following the concept of form follows
function, we assume that existing object shapes were rationally chosen to
provide desired functionalities. First, we use an artificial neural network to
learn a function-to-form mapping by analysing a dataset of objects labeled with
their functionalities. Then, we combine forms providing one or more desired
functions, generating an object shape that is expected to provide all of them.
Finally, we verify in simulation whether the generated object possesses the
desired functionalities, by defining and executing functionality tests on it.
| null |
http://arxiv.org/abs/1805.11984v2
|
http://arxiv.org/pdf/1805.11984v2.pdf
| null |
[
"Mihai Andries",
"Atabak Dehban",
"José Santos-Victor"
] |
[
"Object"
] | 2018-05-30T00:00:00 |
https://openreview.net/forum?id=B1x0enCcK7
|
https://openreview.net/pdf?id=B1x0enCcK7
| null | null |
[] |
https://paperswithcode.com/paper/molgan-an-implicit-generative-model-for-small
|
1805.11973
| null | null |
MolGAN: An implicit generative model for small molecular graphs
|
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to 100% valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse. Code at https://github.com/nicola-decao/MolGAN
|
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures.
|
https://arxiv.org/abs/1805.11973v2
|
https://arxiv.org/pdf/1805.11973v2.pdf
| null |
[
"Nicola De Cao",
"Thomas Kipf"
] |
[
"Graph Matching",
"Reinforcement Learning",
"valid"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-large-scale-data-acquisition-via
|
1805.11970
| null | null |
Automatic Large-Scale Data Acquisition via Crowdsourcing for Crosswalk Classification: A Deep Learning Approach
|
Correctly identifying crosswalks is an essential task for the driving
activity and mobility autonomy. Many crosswalk classification, detection and
localization systems have been proposed in the literature over the years. These
systems use different perspectives to tackle the crosswalk classification
problem: satellite imagery, cockpit view (from the top of a car or behind the
windshield), and pedestrian perspective. Most of the works in the literature
are designed and evaluated using small and local datasets, i.e. datasets that
present low diversity. Scaling to large datasets imposes a challenge for the
annotation procedure. Moreover, there is still need for cross-database
experiments in the literature because it is usually hard to collect the data in
the same place and conditions of the final application. In this paper, we
present a crosswalk classification system based on deep learning. For that,
crowdsourcing platforms, such as OpenStreetMap and Google Street View, are
exploited to enable automatic training via automatic acquisition and annotation
of a large-scale database. Additionally, this work proposes a comparison study
of models trained using fully-automatic data acquisition and annotation against
models that were partially annotated. Cross-database experiments were also
included in the experimentation to show that the proposed methods enable use
with real world applications. Our results show that the model trained on the
fully-automatic database achieved high overall accuracy (94.12%), and that a
statistically significant improvement (to 96.30%) can be achieved by manually
annotating a specific part of the database. Finally, the results of the
cross-database experiments show that both models are robust to the many
variations of image and scenarios, presenting a consistent behavior.
| null |
http://arxiv.org/abs/1805.11970v1
|
http://arxiv.org/pdf/1805.11970v1.pdf
| null |
[
"Rodrigo F. Berriel",
"Franco Schmidt Rossi",
"Alberto F. de Souza",
"Thiago Oliveira-Santos"
] |
[
"General Classification"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/short-term-load-forecasting-with-deep
|
1805.11956
| null | null |
Short-term Load Forecasting with Deep Residual Networks
|
We present in this paper a model for forecasting short-term power loads based
on deep residual networks. The proposed model is able to integrate domain
knowledge and researchers' understanding of the task by virtue of different
neural network building blocks. Specifically, a modified deep residual network
is formulated to improve the forecast results. Further, a two-stage ensemble
strategy is used to enhance the generalization capability of the proposed
model. We also apply the proposed model to probabilistic load forecasting using
Monte Carlo dropout. Three public datasets are used to prove the effectiveness
of the proposed model. Multiple test cases and comparison with existing models
show that the proposed model is able to provide accurate load forecasting
results and has high generalization capability.
|
We present in this paper a model for forecasting short-term power loads based on deep residual networks.
|
http://arxiv.org/abs/1805.11956v1
|
http://arxiv.org/pdf/1805.11956v1.pdf
| null |
[
"Kunjin Chen",
"Kunlong Chen",
"Qin Wang",
"Ziyu He",
"Jun Hu",
"Jinliang He"
] |
[
"Load Forecasting"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-l1-minimization-algorithm-for-sparse
|
1805.11949
| null | null |
Fast L1-Minimization Algorithm for Sparse Approximation Based on an Improved LPNN-LCA framework
|
The aim of sparse approximation is to estimate a sparse signal according to
the measurement matrix and an observation vector. It is widely used in data
analytics, image processing, and communication, etc. Up to now, a lot of
research has been done in this area, and many off-the-shelf algorithms have
been proposed. However, most of them cannot offer a real-time solution. To some
extent, this shortcoming limits its application prospects. To address this
issue, we devise a novel sparse approximation algorithm based on Lagrange
programming neural network (LPNN), locally competitive algorithm (LCA), and
projection theorem. LPNN and LCA are both analog neural network which can help
us get a real-time solution. The non-differentiable objective function can be
solved by the concept of LCA. Utilizing the projection theorem, we further
modify the dynamics and proposed a new system with global asymptotic stability.
Simulation results show that the proposed sparse approximation method has the
real-time solutions with satisfactory MSEs.
| null |
http://arxiv.org/abs/1805.11949v1
|
http://arxiv.org/pdf/1805.11949v1.pdf
| null |
[
"Hao Wang",
"Ruibin Feng",
"Chi-Sing Leung"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/character-level-models-versus-morphology-in
|
1805.11937
| null | null |
Character-Level Models versus Morphology in Semantic Role Labeling
|
Character-level models have become a popular approach specially for their
accessibility and ability to handle unseen data. However, little is known on
their ability to reveal the underlying morphological structure of a word, which
is a crucial skill for high-level semantic analysis tasks, such as semantic
role labeling (SRL). In this work, we train various types of SRL models that
use word, character and morphology level information and analyze how
performance of characters compare to words and morphology for several
languages. We conduct an in-depth error analysis for each morphological
typology and analyze the strengths and limitations of character-level models
that relate to out-of-domain data, training data size, long range dependencies
and model complexity. Our exhaustive analyses shed light on important
characteristics of character-level models and their semantic capability.
|
Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data.
|
http://arxiv.org/abs/1805.11937v1
|
http://arxiv.org/pdf/1805.11937v1.pdf
|
ACL 2018 7
|
[
"Gözde Gül Şahin",
"Mark Steedman"
] |
[
"Semantic Role Labeling"
] | 2018-05-30T00:00:00 |
https://aclanthology.org/P18-1036
|
https://aclanthology.org/P18-1036.pdf
|
character-level-models-versus-morphology-in-1
| null |
[] |
https://paperswithcode.com/paper/transformer-for-emotion-recognition
|
1805.02489
| null | null |
Transformer for Emotion Recognition
|
This paper describes the UMONS solution for the OMG-Emotion Challenge. We
explore a context-dependent architecture where the arousal and valence of an
utterance are predicted according to its surrounding context (i.e. the
preceding and following utterances of the video). We report an improvement when
taking into account context for both unimodal and multimodal predictions.
|
This paper describes the UMONS solution for the OMG-Emotion Challenge.
|
http://arxiv.org/abs/1805.02489v2
|
http://arxiv.org/pdf/1805.02489v2.pdf
| null |
[
"Jean-Benoit Delbrouck"
] |
[
"Emotion Recognition"
] | 2018-05-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-to-generate-facial-depth-maps
|
1805.11927
| null | null |
Learning to Generate Facial Depth Maps
|
In this paper, an adversarial architecture for facial depth map estimation
from monocular intensity images is presented. By following an image-to-image
approach, we combine the advantages of supervised learning and adversarial
training, proposing a conditional Generative Adversarial Network that
effectively learns to translate intensity face images into the corresponding
depth maps. Two public datasets, namely Biwi database and Pandora dataset, are
exploited to demonstrate that the proposed model generates high-quality
synthetic depth images, both in terms of visual appearance and informative
content. Furthermore, we show that the model is capable of predicting
distinctive facial details by testing the generated depth maps through a deep
model trained on authentic depth maps for the face verification task.
| null |
http://arxiv.org/abs/1805.11927v1
|
http://arxiv.org/pdf/1805.11927v1.pdf
| null |
[
"Stefano Pini",
"Filippo Grazioli",
"Guido Borghi",
"Roberto Vezzani",
"Rita Cucchiara"
] |
[
"Face Verification",
"Generative Adversarial Network"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-survey-of-dimensionality-reduction-1
|
1706.04371
| null | null |
A survey of dimensionality reduction techniques based on random projection
|
Dimensionality reduction techniques play important roles in the analysis of
big data. Traditional dimensionality reduction approaches, such as principal
component analysis (PCA) and linear discriminant analysis (LDA), have been
studied extensively in the past few decades. However, as the dimensionality of
data increases, the computational cost of traditional dimensionality reduction
methods grows exponentially, and the computation becomes prohibitively
intractable. These drawbacks have triggered the development of random
projection (RP) techniques, which map high-dimensional data onto a
low-dimensional subspace with extremely reduced time cost. However, the RP
transformation matrix is generated without considering the intrinsic structure
of the original data and usually leads to relatively high distortion.
Therefore, in recent years, methods based on RP have been proposed to address
this problem. In this paper, we summarize the methods used in different
situations to help practitioners to employ the proper techniques for their
specific applications. Meanwhile, we enumerate the benefits and limitations of
the various methods and provide further references for researchers to develop
novel RP-based approaches.
| null |
http://arxiv.org/abs/1706.04371v4
|
http://arxiv.org/pdf/1706.04371v4.pdf
| null |
[
"Haozhe Xie",
"Jie Li",
"Hanqing Xue"
] |
[
"Dimensionality Reduction",
"Survey"
] | 2017-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/additive-margin-softmax-for-face-verification
|
1801.05599
| null | null |
Additive Margin Softmax for Face Verification
|
In this paper, we propose a conceptually simple and geometrically
interpretable objective function, i.e. additive margin Softmax (AM-Softmax),
for deep face verification. In general, the face verification task can be
viewed as a metric learning problem, so learning large-margin face features
whose intra-class variation is small and inter-class difference is large is of
great importance in order to achieve good performance. Recently, Large-margin
Softmax and Angular Softmax have been proposed to incorporate the angular
margin in a multiplicative manner. In this work, we introduce a novel additive
angular margin for the Softmax loss, which is intuitively appealing and more
interpretable than the existing works. We also emphasize and discuss the
importance of feature normalization in the paper. Most importantly, our
experiments on LFW BLUFR and MegaFace show that our additive margin softmax
loss consistently performs better than the current state-of-the-art methods
using the same network architecture and training dataset. Our code has also
been made available at https://github.com/happynear/AMSoftmax
|
In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.
|
http://arxiv.org/abs/1801.05599v4
|
http://arxiv.org/pdf/1801.05599v4.pdf
| null |
[
"Feng Wang",
"Weiyang Liu",
"Haijun Liu",
"Jian Cheng"
] |
[
"Face Verification",
"Metric Learning"
] | 2018-01-17T00:00:00 | 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/multiple-manifolds-metric-learning-with
|
1805.11918
| null | null |
Multiple Manifolds Metric Learning with Application to Image Set Classification
|
In image set classification, a considerable advance has been made by modeling
the original image sets by second order statistics or linear subspace, which
typically lie on the Riemannian manifold. Specifically, they are Symmetric
Positive Definite (SPD) manifold and Grassmann manifold respectively, and some
algorithms have been developed on them for classification tasks. Motivated by
the inability of existing methods to extract discriminatory features for data
on Riemannian manifolds, we propose a novel algorithm which combines multiple
manifolds as the features of the original image sets. In order to fuse these
manifolds, the well-studied Riemannian kernels have been utilized to map the
original Riemannian spaces into high dimensional Hilbert spaces. A metric
Learning method has been devised to embed these kernel spaces into a lower
dimensional common subspace for classification. The state-of-the-art results
achieved on three datasets corresponding to two different classification tasks,
namely face recognition and object categorization, demonstrate the
effectiveness of the proposed method.
| null |
http://arxiv.org/abs/1805.11918v1
|
http://arxiv.org/pdf/1805.11918v1.pdf
| null |
[
"Rui Wang",
"Xiao-Jun Wu",
"Kai-Xuan Chen",
"Josef Kittler"
] |
[
"Classification",
"Face Recognition",
"General Classification",
"Metric Learning",
"Object Categorization"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-dynamics-of-learning-a-random-matrix
|
1805.11917
| null | null |
The Dynamics of Learning: A Random Matrix Approach
|
Understanding the learning dynamics of neural networks is one of the key
issues for the improvement of optimization algorithms as well as for the
theoretical comprehension of why deep neural nets work so well today. In this
paper, we introduce a random matrix-based framework to analyze the learning
dynamics of a single-layer linear network on a binary classification problem,
for data of simultaneously large dimension and size, trained by gradient
descent. Our results provide rich insights into common questions in neural
nets, such as overfitting, early stopping and the initialization of training,
thereby opening the door for future studies of more elaborate structures and
models appearing in today's neural networks.
| null |
http://arxiv.org/abs/1805.11917v2
|
http://arxiv.org/pdf/1805.11917v2.pdf
|
ICML 2018 7
|
[
"Zhenyu Liao",
"Romain Couillet"
] |
[
"Binary Classification",
"General Classification"
] | 2018-05-30T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2023
|
http://proceedings.mlr.press/v80/liao18b/liao18b.pdf
|
the-dynamics-of-learning-a-random-matrix-1
| null |
[
{
"code_snippet_url": "",
"description": "**Early Stopping** is a regularization technique for deep neural networks that stops training when parameter updates no longer begin to yield improves on a validation set. In essence, we store and update the current best parameters during training, and when parameter updates no longer yield an improvement (after a set number of iterations) we stop training and use the last best parameters. It works as a regularizer by restricting the optimization procedure to a smaller volume of parameter space.\r\n\r\nImage Source: [Ramazan Gençay](https://www.researchgate.net/figure/Early-stopping-based-on-cross-validation_fig1_3302948)",
"full_name": "Early Stopping",
"introduced_year": 1995,
"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": "Early Stopping",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/unsupervised-detection-of-diachronic-word
|
1805.11295
| null | null |
Unsupervised detection of diachronic word sense evolution
|
Most words have several senses and connotations which evolve in time due to
semantic shift, so that closely related words may gain different or even
opposite meanings over the years. This evolution is very relevant to the study
of language and of cultural changes, but the tools currently available for
diachronic semantic analysis have significant, inherent limitations and are not
suitable for real-time analysis. In this article, we demonstrate how the
linearity of random vectors techniques enables building time series of
congruent word embeddings (or semantic spaces) which can then be compared and
combined linearly without loss of precision over any time period to detect
diachronic semantic shifts. We show how this approach yields time trajectories
of polysemous words such as amazon or apple, enables following semantic drifts
and gender bias across time, reveals the shifting instantiations of stable
concepts such as hurricane or president. This very fast, linear approach can
easily be distributed over many processors to follow in real time streams of
social media such as Twitter or Facebook; the resulting, time-dependent
semantic spaces can then be combined at will by simple additions or
subtractions.
| null |
http://arxiv.org/abs/1805.11295v2
|
http://arxiv.org/pdf/1805.11295v2.pdf
| null |
[
"Jean-François Delpech"
] |
[
"Time Series",
"Time Series Analysis",
"Word Embeddings"
] | 2018-05-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-spectrum-of-random-features-maps-of
|
1805.11916
| null | null |
On the Spectrum of Random Features Maps of High Dimensional Data
|
Random feature maps are ubiquitous in modern statistical machine learning,
where they generalize random projections by means of powerful, yet often
difficult to analyze nonlinear operators. In this paper, we leverage the
"concentration" phenomenon induced by random matrix theory to perform a
spectral analysis on the Gram matrix of these random feature maps, here for
Gaussian mixture models of simultaneously large dimension and size. Our results
are instrumental to a deeper understanding on the interplay of the nonlinearity
and the statistics of the data, thereby allowing for a better tuning of random
feature-based techniques.
|
Random feature maps are ubiquitous in modern statistical machine learning, where they generalize random projections by means of powerful, yet often difficult to analyze nonlinear operators.
|
http://arxiv.org/abs/1805.11916v2
|
http://arxiv.org/pdf/1805.11916v2.pdf
|
ICML 2018 7
|
[
"Zhenyu Liao",
"Romain Couillet"
] |
[
"BIG-bench Machine Learning",
"Vocal Bursts Intensity Prediction"
] | 2018-05-30T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1898
|
http://proceedings.mlr.press/v80/liao18a/liao18a.pdf
|
on-the-spectrum-of-random-features-maps-of-1
| null |
[] |
https://paperswithcode.com/paper/bruno-a-deep-recurrent-model-for-exchangeable
|
1802.07535
| null | null |
BRUNO: A Deep Recurrent Model for Exchangeable Data
|
We present a novel model architecture which leverages deep learning tools to
perform exact Bayesian inference on sets of high dimensional, complex
observations. Our model is provably exchangeable, meaning that the joint
distribution over observations is invariant under permutation: this property
lies at the heart of Bayesian inference. The model does not require variational
approximations to train, and new samples can be generated conditional on
previous samples, with cost linear in the size of the conditioning set. The
advantages of our architecture are demonstrated on learning tasks that require
generalisation from short observed sequences while modelling sequence
variability, such as conditional image generation, few-shot learning, and
anomaly detection.
|
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
|
http://arxiv.org/abs/1802.07535v3
|
http://arxiv.org/pdf/1802.07535v3.pdf
|
NeurIPS 2018 12
|
[
"Iryna Korshunova",
"Jonas Degrave",
"Ferenc Huszár",
"Yarin Gal",
"Arthur Gretton",
"Joni Dambre"
] |
[
"Anomaly Detection",
"Bayesian Inference",
"Conditional Image Generation",
"Few-Shot Learning",
"Image Generation",
"model"
] | 2018-02-21T00:00:00 |
http://papers.nips.cc/paper/7949-bruno-a-deep-recurrent-model-for-exchangeable-data
|
http://papers.nips.cc/paper/7949-bruno-a-deep-recurrent-model-for-exchangeable-data.pdf
|
bruno-a-deep-recurrent-model-for-exchangeable-1
| null |
[] |
https://paperswithcode.com/paper/dynamic-texture-recognition-using-time-causal
|
1710.04842
| null | null |
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
|
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.
| null |
http://arxiv.org/abs/1710.04842v3
|
http://arxiv.org/pdf/1710.04842v3.pdf
| null |
[
"Ylva Jansson",
"Tony Lindeberg"
] |
[
"Descriptive",
"Dynamic Texture Recognition",
"Object Recognition"
] | 2017-10-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/needle-tip-force-estimation-using-an-oct
|
1805.11911
| null | null |
Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture
|
Needle insertion is common during minimally invasive interventions such as
biopsy or brachytherapy. During soft tissue needle insertion, forces acting at
the needle tip cause tissue deformation and needle deflection. Accurate needle
tip force measurement provides information on needle-tissue interaction and
helps detecting and compensating potential misplacement. For this purpose we
introduce an image-based needle tip force estimation method using an optical
fiber imaging the deformation of an epoxy layer below the needle tip over time.
For calibration and force estimation, we introduce a novel deep learning-based
fused convolutional GRU-CNN model which effectively exploits the
spatio-temporal data structure. The needle is easy to manufacture and our model
achieves a mean absolute error of 1.76 +- 1.5 mN with a cross-correlation
coefficient of 0.9996, clearly outperforming other methods. We test needles
with different materials to demonstrate that the approach can be adapted for
different sensitivities and force ranges. Furthermore, we validate our approach
in an ex-vivo prostate needle insertion scenario.
| null |
http://arxiv.org/abs/1805.11911v1
|
http://arxiv.org/pdf/1805.11911v1.pdf
| null |
[
"Nils Gessert",
"Torben Priegnitz",
"Thore Saathoff",
"Sven-Thomas Antoni",
"David Meyer",
"Moritz Franz Hamann",
"Klaus-Peter Jünemann",
"Christoph Otte",
"Alexander Schlaefer"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/who-learns-better-bayesian-network-structures
|
1805.11908
| null | null |
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
|
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches. Constraint-based and score-based algorithms have been shown to learn the same structures when conditional independence and goodness of fit are both assessed using entropy and the topological ordering of the network is known (Cowell, 2001). In this paper, we investigate how these three classes of algorithms perform outside the assumptions above in terms of speed and accuracy of network reconstruction for both discrete and Gaussian Bayesian networks. We approach this question by recognising that structure learning is defined by the combination of a statistical criterion and an algorithm that determines how the criterion is applied to the data. Removing the confounding effect of different choices for the statistical criterion, we find using both simulated and real-world complex data that constraint-based algorithms are often less accurate than score-based algorithms, but are seldom faster (even at large sample sizes); and that hybrid algorithms are neither faster nor more accurate than constraint-based algorithms. This suggests that commonly held beliefs on structure learning in the literature are strongly influenced by the choice of particular statistical criteria rather than just by the properties of the algorithms themselves.
| null |
https://arxiv.org/abs/1805.11908v3
|
https://arxiv.org/pdf/1805.11908v3.pdf
| null |
[
"Marco Scutari",
"Catharina Elisabeth Graafland",
"José Manuel Gutiérrez"
] |
[] | 2018-05-30T00:00:00 | 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/differential-properties-of-sinkhorn
|
1805.11897
| null | null |
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
|
Applications of optimal transport have recently gained remarkable attention
thanks to the computational advantages of entropic regularization. However, in
most situations the Sinkhorn approximation of the Wasserstein distance is
replaced by a regularized version that is less accurate but easy to
differentiate. In this work we characterize the differential properties of the
original Sinkhorn distance, proving that it enjoys the same smoothness as its
regularized version and we explicitly provide an efficient algorithm to compute
its gradient. We show that this result benefits both theory and applications:
on one hand, high order smoothness confers statistical guarantees to learning
with Wasserstein approximations. On the other hand, the gradient formula allows
us to efficiently solve learning and optimization problems in practice.
Promising preliminary experiments complement our analysis.
|
Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization.
|
http://arxiv.org/abs/1805.11897v1
|
http://arxiv.org/pdf/1805.11897v1.pdf
|
NeurIPS 2018 12
|
[
"Giulia Luise",
"Alessandro Rudi",
"Massimiliano Pontil",
"Carlo Ciliberto"
] |
[] | 2018-05-30T00:00:00 |
http://papers.nips.cc/paper/7827-differential-properties-of-sinkhorn-approximation-for-learning-with-wasserstein-distance
|
http://papers.nips.cc/paper/7827-differential-properties-of-sinkhorn-approximation-for-learning-with-wasserstein-distance.pdf
|
differential-properties-of-sinkhorn-1
| null |
[] |
https://paperswithcode.com/paper/note-on-representing-attribute-reduction-and
|
1711.05509
| null | null |
Note on Representing attribute reduction and concepts in concepts lattice using graphs
|
Mao H. (2017, Representing attribute reduction and concepts in concept
lattice using graphs. Soft Computing 21(24):7293--7311) claims to make
contributions to the study of reduction of attributes in concept lattices by
using graph theory. We show that her results are either trivial or already
well-known and all three algorithms proposed in the paper are incorrect.
| null |
http://arxiv.org/abs/1711.05509v2
|
http://arxiv.org/pdf/1711.05509v2.pdf
| null |
[
"Jan Konecny"
] |
[
"Attribute"
] | 2017-11-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tile2vec-unsupervised-representation-learning
|
1805.02855
| null | null |
Tile2Vec: Unsupervised representation learning for spatially distributed data
|
Geospatial analysis lacks methods like the word vector representations and
pre-trained networks that significantly boost performance across a wide range
of natural language and computer vision tasks. To fill this gap, we introduce
Tile2Vec, an unsupervised representation learning algorithm that extends the
distributional hypothesis from natural language -- words appearing in similar
contexts tend to have similar meanings -- to spatially distributed data. We
demonstrate empirically that Tile2Vec learns semantically meaningful
representations on three datasets. Our learned representations significantly
improve performance in downstream classification tasks and, similar to word
vectors, visual analogies can be obtained via simple arithmetic in the latent
space.
|
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
|
http://arxiv.org/abs/1805.02855v2
|
http://arxiv.org/pdf/1805.02855v2.pdf
| null |
[
"Neal Jean",
"Sherrie Wang",
"Anshul Samar",
"George Azzari",
"David Lobell",
"Stefano Ermon"
] |
[
"General Classification",
"Representation Learning",
"Visual Analogies"
] | 2018-05-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/predictive-performance-modeling-for
|
1805.11877
| null | null |
Predictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning
|
In many domains, the previous decade was characterized by increasing data
volumes and growing complexity of computational workloads, creating new demands
for highly data-parallel computing in distributed systems. Effective operation
of these systems is challenging when facing uncertainties about the performance
of jobs and tasks under varying resource configurations, e.g., for scheduling
and resource allocation. We survey predictive performance modeling (PPM)
approaches to estimate performance metrics such as execution duration, required
memory or wait times of future jobs and tasks based on past performance
observations. We focus on non-intrusive methods, i.e., methods that can be
applied to any workload without modification, since the workload is usually a
black-box from the perspective of the systems managing the computational
infrastructure. We classify and compare sources of performance variation,
predicted performance metrics, required training data, use cases, and the
underlying prediction techniques. We conclude by identifying several open
problems and pressing research needs in the field.
| null |
http://arxiv.org/abs/1805.11877v1
|
http://arxiv.org/pdf/1805.11877v1.pdf
| null |
[
"Carl Witt",
"Marc Bux",
"Wladislaw Gusew",
"Ulf Leser"
] |
[
"BIG-bench Machine Learning",
"Distributed Computing",
"Scheduling"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/emotionlines-an-emotion-corpus-of-multi-party
|
1802.08379
| null | null |
EmotionLines: An Emotion Corpus of Multi-Party Conversations
|
Feeling emotion is a critical characteristic to distinguish people from
machines. Among all the multi-modal resources for emotion detection, textual
datasets are those containing the least additional information in addition to
semantics, and hence are adopted widely for testing the developed systems.
However, most of the textual emotional datasets consist of emotion labels of
only individual words, sentences or documents, which makes it challenging to
discuss the contextual flow of emotions. In this paper, we introduce
EmotionLines, the first dataset with emotions labeling on all utterances in
each dialogue only based on their textual content. Dialogues in EmotionLines
are collected from Friends TV scripts and private Facebook messenger dialogues.
Then one of seven emotions, six Ekman's basic emotions plus the neutral
emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245
utterances from 2,000 dialogues are labeled in EmotionLines. We also provide
several strong baselines for emotion detection models on EmotionLines in this
paper.
| null |
http://arxiv.org/abs/1802.08379v2
|
http://arxiv.org/pdf/1802.08379v2.pdf
|
LREC 2018 5
|
[
"Sheng-Yeh Chen",
"Chao-Chun Hsu",
"Chuan-Chun Kuo",
"Ting-Hao",
"Huang",
"Lun-Wei Ku"
] |
[] | 2018-02-23T00:00:00 |
https://aclanthology.org/L18-1252
|
https://aclanthology.org/L18-1252.pdf
|
emotionlines-an-emotion-corpus-of-multi-party-1
| null |
[] |
https://paperswithcode.com/paper/fast-and-accurate-low-rank-factorization-of
|
1706.08146
| null | null |
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
|
What learning algorithms can be run directly on compressively-sensed data? In this work, we consider the question of accurately and efficiently computing low-rank matrix or tensor factorizations given data compressed via random projections. We examine the approach of first performing factorization in the compressed domain, and then reconstructing the original high-dimensional factors from the recovered (compressed) factors. In both the matrix and tensor settings, we establish conditions under which this natural approach will provably recover the original factors. While it is well-known that random projections preserve a number of geometric properties of a dataset, our work can be viewed as showing that they can also preserve certain solutions of non-convex, NP-Hard problems like non-negative matrix factorization. We support these theoretical results with experiments on synthetic data and demonstrate the practical applicability of compressed factorization on real-world gene expression and EEG time series datasets.
| null |
https://arxiv.org/abs/1706.08146v3
|
https://arxiv.org/pdf/1706.08146v3.pdf
| null |
[
"Vatsal Sharan",
"Kai Sheng Tai",
"Peter Bailis",
"Gregory Valiant"
] |
[
"EEG",
"Electroencephalogram (EEG)",
"Time Series",
"Time Series Analysis"
] | 2017-06-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-corpus-of-english-hindi-code-mixed-tweets
|
1805.11869
| null | null |
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
|
Social media platforms like twitter and facebook have be- come two of the
largest mediums used by people to express their views to- wards different
topics. Generation of such large user data has made NLP tasks like sentiment
analysis and opinion mining much more important. Using sarcasm in texts on
social media has become a popular trend lately. Using sarcasm reverses the
meaning and polarity of what is implied by the text which poses challenge for
many NLP tasks. The task of sarcasm detection in text is gaining more and more
importance for both commer- cial and security services. We present the first
English-Hindi code-mixed dataset of tweets marked for presence of sarcasm and
irony where each token is also annotated with a language tag. We present a
baseline su- pervised classification system developed using the same dataset
which achieves an average F-score of 78.4 after using random forest classifier
and performing 10-fold cross validation.
|
Social media platforms like twitter and facebook have be- come two of the largest mediums used by people to express their views to- wards different topics.
|
http://arxiv.org/abs/1805.11869v1
|
http://arxiv.org/pdf/1805.11869v1.pdf
| null |
[
"Sahil Swami",
"Ankush Khandelwal",
"Vinay Singh",
"Syed Sarfaraz Akhtar",
"Manish Shrivastava"
] |
[
"Opinion Mining",
"Sarcasm Detection",
"Sentiment Analysis",
"TAG"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/competitive-training-of-mixtures-of
|
1804.11130
| null | null |
Competitive Training of Mixtures of Independent Deep Generative Models
|
A common assumption in causal modeling posits that the data is generated by a
set of independent mechanisms, and algorithms should aim to recover this
structure. Standard unsupervised learning, however, is often concerned with
training a single model to capture the overall distribution or aspects thereof.
Inspired by clustering approaches, we consider mixtures of implicit generative
models that ``disentangle'' the independent generative mechanisms underlying
the data. Relying on an additional set of discriminators, we propose a
competitive training procedure in which the models only need to capture the
portion of the data distribution from which they can produce realistic samples.
As a by-product, each model is simpler and faster to train. We empirically show
that our approach splits the training distribution in a sensible way and
increases the quality of the generated samples.
| null |
http://arxiv.org/abs/1804.11130v4
|
http://arxiv.org/pdf/1804.11130v4.pdf
| null |
[
"Francesco Locatello",
"Damien Vincent",
"Ilya Tolstikhin",
"Gunnar Rätsch",
"Sylvain Gelly",
"Bernhard Schölkopf"
] |
[
"Clustering"
] | 2018-04-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/an-english-hindi-code-mixed-corpus-stance
|
1805.11868
| null | null |
An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System
|
Social media has become one of the main channels for peo- ple to communicate
and share their views with the society. We can often detect from these views
whether the person is in favor, against or neu- tral towards a given topic.
These opinions from social media are very useful for various companies. We
present a new dataset that consists of 3545 English-Hindi code-mixed tweets
with opinion towards Demoneti- sation that was implemented in India in 2016
which was followed by a large countrywide debate. We present a baseline
supervised classification system for stance detection developed using the same
dataset that uses various machine learning techniques to achieve an accuracy of
58.7% on 10-fold cross validation.
| null |
http://arxiv.org/abs/1805.11868v1
|
http://arxiv.org/pdf/1805.11868v1.pdf
| null |
[
"Sahil Swami",
"Ankush Khandelwal",
"Vinay Singh",
"Syed Sarfaraz Akhtar",
"Manish Shrivastava"
] |
[
"General Classification",
"Stance Detection"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/using-inter-sentence-diverse-beam-search-to
|
1805.11867
| null | null |
Using Inter-Sentence Diverse Beam Search to Reduce Redundancy in Visual Storytelling
|
Visual storytelling includes two important parts: coherence between the story
and images as well as the story structure. For image to text neural network
models, similar images in the sequence would provide close information for
story generator to obtain almost identical sentence. However, repeatedly
narrating same objects or events will undermine a good story structure. In this
paper, we proposed an inter-sentence diverse beam search to generate a more
expressive story. Comparing to some recent models of visual storytelling task,
which generate story without considering the generated sentence of the previous
picture, our proposed method can avoid generating identical sentence even given
a sequence of similar pictures.
| null |
http://arxiv.org/abs/1805.11867v1
|
http://arxiv.org/pdf/1805.11867v1.pdf
| null |
[
"Chao-Chun Hsu",
"Szu-Min Chen",
"Ming-Hsun Hsieh",
"Lun-Wei Ku"
] |
[
"Image to text",
"Sentence",
"Visual Storytelling"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/foresee-attentive-future-projections-of
|
1805.11861
| null | null |
Foresee: Attentive Future Projections of Chaotic Road Environments with Online Training
|
In this paper, we train a recurrent neural network to learn dynamics of a
chaotic road environment and to project the future of the environment on an
image. Future projection can be used to anticipate an unseen environment for
example, in autonomous driving. Road environment is highly dynamic and complex
due to the interaction among traffic participants such as vehicles and
pedestrians. Even in this complex environment, a human driver is efficacious to
safely drive on chaotic roads irrespective of the number of traffic
participants. The proliferation of deep learning research has shown the
efficacy of neural networks in learning this human behavior. In the same
direction, we investigate recurrent neural networks to understand the chaotic
road environment which is shared by pedestrians, vehicles (cars, trucks,
bicycles etc.), and sometimes animals as well. We propose \emph{Foresee}, a
unidirectional gated recurrent units (GRUs) network with attention to project
future of the environment in the form of images. We have collected several
videos on Delhi roads consisting of various traffic participants, background
and infrastructure differences (like 3D pedestrian crossing) at various times
on various days. We train \emph{Foresee} in an unsupervised way and we use
online training to project frames up to $0.5$ seconds in advance. We show that
our proposed model performs better than state of the art methods (prednet and
Enc. Dec. LSTM) and finally, we show that our trained model generalizes to a
public dataset for future projections.
| null |
http://arxiv.org/abs/1805.11861v1
|
http://arxiv.org/pdf/1805.11861v1.pdf
| null |
[
"Anil Sharma",
"Prabhat Kumar"
] |
[
"Autonomous Driving"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/runresidual-u-net-for-computer-aided
|
1805.11856
| null | null |
RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection
|
The early detection and early diagnosis of lung cancer are crucial to improve
the survival rate of lung cancer patients. Pulmonary nodules detection results
have a significant impact on the later diagnosis. In this work, we propose a
new network named RUN to complete nodule detection in a single step by
bypassing the candidate selection. The system introduces the shortcut of the
residual network to improve the traditional U-Net, thereby solving the
disadvantage of poor results due to its lack of depth. Furthermore, we compare
the experimental results with the traditional U-Net. We validate our method in
LUng Nodule Analysis 2016 (LUNA16) Nodule Detection Challenge. We acquire a
sensitivity of 90.90% at 2 false positives per scan and therefore achieve
better performance than the current state-of-the-art approaches.
| null |
http://arxiv.org/abs/1805.11856v1
|
http://arxiv.org/pdf/1805.11856v1.pdf
| null |
[
"Tian Lan",
"Yuanyuan Li",
"Jonah Kimani Murugi",
"Yi Ding",
"Zhiguang Qin"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": 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
},
{
"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": "",
"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/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"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": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
}
] |
https://paperswithcode.com/paper/labelling-as-an-unsupervised-learning-problem
|
1805.03911
| null | null |
Labelling as an unsupervised learning problem
|
Unravelling hidden patterns in datasets is a classical problem with many
potential applications. In this paper, we present a challenge whose objective
is to discover nonlinear relationships in noisy cloud of points. If a set of
point satisfies a nonlinear relationship that is unlikely to be due to
randomness, we will label the set with this relationship. Since points can
satisfy one, many or no such nonlinear relationships, cloud of points will
typically have one, multiple or no labels at all. This introduces the labelling
problem that will be studied in this paper.
The objective of this paper is to develop a framework for the labelling
problem. We introduce a precise notion of a label, and we propose an algorithm
to discover such labels in a given dataset, which is then tested in synthetic
datasets. We also analyse, using tools from random matrix theory, the problem
of discovering false labels in the dataset.
| null |
http://arxiv.org/abs/1805.03911v2
|
http://arxiv.org/pdf/1805.03911v2.pdf
| null |
[
"Terry Lyons",
"Imanol Perez Arribas"
] |
[] | 2018-05-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adagio-interactive-experimentation-with
|
1805.11852
| null | null |
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
|
Adversarial machine learning research has recently demonstrated the
feasibility to confuse automatic speech recognition (ASR) models by introducing
acoustically imperceptible perturbations to audio samples. To help researchers
and practitioners gain better understanding of the impact of such attacks, and
to provide them with tools to help them more easily evaluate and craft strong
defenses for their models, we present ADAGIO, the first tool designed to allow
interactive experimentation with adversarial attacks and defenses on an ASR
model in real time, both visually and aurally. ADAGIO incorporates AMR and MP3
audio compression techniques as defenses, which users can interactively apply
to attacked audio samples. We show that these techniques, which are based on
psychoacoustic principles, effectively eliminate targeted attacks, reducing the
attack success rate from 92.5% to 0%. We will demonstrate ADAGIO and invite the
audience to try it on the Mozilla Common Voice dataset.
| null |
http://arxiv.org/abs/1805.11852v1
|
http://arxiv.org/pdf/1805.11852v1.pdf
| null |
[
"Nilaksh Das",
"Madhuri Shanbhogue",
"Shang-Tse Chen",
"Li Chen",
"Michael E. Kounavis",
"Duen Horng Chau"
] |
[
"Adversarial Attack",
"Audio Compression",
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"speech-recognition",
"Speech Recognition"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spectral-spatial-classification-of-1
|
1711.05512
| null | null |
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
|
Spectral-spatial classification of hyperspectral images has been the subject
of many studies in recent years. In the presence of only very few labeled
pixels, this task becomes challenging. In this paper we address the following
two research questions: 1) Can a simple neural network with just a single
hidden layer achieve state of the art performance in the presence of few
labeled pixels? 2) How is the performance of hyperspectral image classification
methods affected when using disjoint train and test sets? We give a positive
answer to the first question by using three tricks within a very basic shallow
Convolutional Neural Network (CNN) architecture: a tailored loss function, and
smooth- and label-based data augmentation. The tailored loss function enforces
that neighborhood wavelengths have similar contributions to the features
generated during training. A new label-based technique here proposed favors
selection of pixels in smaller classes, which is beneficial in the presence of
very few labeled pixels and skewed class distributions. To address the second
question, we introduce a new sampling procedure to generate disjoint train and
test set. Then the train set is used to obtain the CNN model, which is then
applied to pixels in the test set to estimate their labels. We assess the
efficacy of the simple neural network method on five publicly available
hyperspectral images. On these images our method significantly outperforms
considered baselines. Notably, with just 1% of labeled pixels per class, on
these datasets our method achieves an accuracy that goes from 86.42%
(challenging dataset) to 99.52% (easy dataset). Furthermore we show that the
simple neural network method improves over other baselines in the new
challenging supervised setting. Our analysis substantiates the highly
beneficial effect of using the entire image (so train and test data) for
constructing a model.
|
2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets?
|
http://arxiv.org/abs/1711.05512v4
|
http://arxiv.org/pdf/1711.05512v4.pdf
| null |
[
"Jacopo Acquarelli",
"Elena Marchiori",
"Lutgarde M. C. Buydens",
"Thanh Tran",
"Twan van Laarhoven"
] |
[
"Data Augmentation",
"General Classification",
"Hyperspectral Image Classification",
"image-classification",
"Image Classification"
] | 2017-11-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-joking-machine-humorous-image
|
1805.11850
| null | null |
Neural Joking Machine : Humorous image captioning
|
What is an effective expression that draws laughter from human beings? In the
present paper, in order to consider this question from an academic standpoint,
we generate an image caption that draws a "laugh" by a computer. A system that
outputs funny captions based on the image caption proposed in the computer
vision field is constructed. Moreover, we also propose the Funny Score, which
flexibly gives weights according to an evaluation database. The Funny Score
more effectively brings out "laughter" to optimize a model. In addition, we
build a self-collected BoketeDB, which contains a theme (image) and funny
caption (text) posted on "Bokete", which is an image Ogiri website. In an
experiment, we use BoketeDB to verify the effectiveness of the proposed method
by comparing the results obtained using the proposed method and those obtained
using MS COCO Pre-trained CNN+LSTM, which is the baseline and idiot created by
humans. We refer to the proposed method, which uses the BoketeDB pre-trained
model, as the Neural Joking Machine (NJM).
| null |
http://arxiv.org/abs/1805.11850v1
|
http://arxiv.org/pdf/1805.11850v1.pdf
| null |
[
"Kota Yoshida",
"Munetaka Minoguchi",
"Kenichiro Wani",
"Akio Nakamura",
"Hirokatsu Kataoka"
] |
[
"Image Captioning"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-instance-aware-object-detection
|
1805.10765
| null | null |
Learning Instance-Aware Object Detection Using Determinantal Point Processes
|
Recent object detectors find instances while categorizing candidate regions. As each region is evaluated independently, the number of candidate regions from a detector is usually larger than the number of objects. Since the final goal of detection is to assign a single detection to each object, a heuristic algorithm, such as non-maximum suppression (NMS), is used to select a single bounding box for an object. While simple heuristic algorithms are effective for stand-alone objects, they can fail to detect overlapped objects. In this paper, we address this issue by training a network to distinguish different objects using the relationship between candidate boxes. We propose an instance-aware detection network (IDNet), which can learn to extract features from candidate regions and measure their similarities. Based on pairwise similarities and detection qualities, the IDNet selects a subset of candidate bounding boxes using instance-aware determinantal point process inference (IDPP). Extensive experiments demonstrate that the proposed algorithm achieves significant improvements for detecting overlapped objects compared to existing state-of-the-art detection methods on the PASCAL VOC and MS COCO datasets.
|
Recent object detectors find instances while categorizing candidate regions.
|
https://arxiv.org/abs/1805.10765v3
|
https://arxiv.org/pdf/1805.10765v3.pdf
| null |
[
"Nuri Kim",
"Donghoon Lee",
"Songhwai Oh"
] |
[
"Object",
"object-detection",
"Object Detection",
"Point Processes"
] | 2018-05-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robot-localisation-and-3d-position-estimation
|
1801.02025
| null | null |
Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks
|
Many works in collaborative robotics and human-robot interaction focuses on
identifying and predicting human behaviour while considering the information
about the robot itself as given. This can be the case when sensors and the
robot are calibrated in relation to each other and often the reconfiguration of
the system is not possible, or extra manual work is required. We present a deep
learning based approach to remove the constraint of having the need for the
robot and the vision sensor to be fixed and calibrated in relation to each
other. The system learns the visual cues of the robot body and is able to
localise it, as well as estimate the position of robot joints in 3D space by
just using a 2D color image. The method uses a cascaded convolutional neural
network, and we present the structure of the network, describe our own
collected dataset, explain the network training and achieved results. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and a good estimate of its joints positions in 3D. The
accuracy is not good enough for visual servoing applications yet, however, it
can be sufficient for general safety and some collaborative tasks not requiring
very high precision. The main benefit of our method is the possibility of the
vision sensor to move freely. This allows it to be mounted on moving objects,
for example, a body of the person or a mobile robot working in the same
environment as the robots are operating in.
| null |
http://arxiv.org/abs/1801.02025v2
|
http://arxiv.org/pdf/1801.02025v2.pdf
| null |
[
"Justinas Miseikis",
"Patrick Knobelreiter",
"Inka Brijacak",
"Saeed Yahyanejad",
"Kyrre Glette",
"Ole Jakob Elle",
"Jim Torresen"
] |
[
"Position"
] | 2018-01-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-multiple-non-mutually-exclusive
|
1805.11837
| null | null |
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels
|
Medical image classification involves thresholding of labels that represent
malignancy risk levels. Usually, a task defines a single threshold, and when
developing computer-aided diagnosis tools, a single network is trained per such
threshold, e.g. as screening out healthy (very low risk) patients to leave
possibly sick ones for further analysis (low threshold), or trying to find
malignant cases among those marked as non-risk by the radiologist ("second
reading", high threshold). We propose a way to rephrase the classification
problem in a manner that yields several problems (corresponding to different
thresholds) to be solved simultaneously. This allows the use of Multiple Task
Learning (MTL) methods, significantly improving the performance of the original
classifier, by facilitating effective extraction of information from existing
data.
| null |
http://arxiv.org/abs/1805.11837v2
|
http://arxiv.org/pdf/1805.11837v2.pdf
| null |
[
"Vadim Ratner",
"Yoel Shoshan",
"Tal Kachman"
] |
[
"General Classification",
"image-classification",
"Image Classification",
"Medical Image Classification"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lag-lazily-aggregated-gradient-for
|
1805.09965
| null | null |
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
|
This paper presents a new class of gradient methods for distributed machine
learning that adaptively skip the gradient calculations to learn with reduced
communication and computation. Simple rules are designed to detect
slowly-varying gradients and, therefore, trigger the reuse of outdated
gradients. The resultant gradient-based algorithms are termed Lazily Aggregated
Gradient --- justifying our acronym LAG used henceforth. Theoretically, the
merits of this contribution are: i) the convergence rate is the same as batch
gradient descent in strongly-convex, convex, and nonconvex smooth cases; and,
ii) if the distributed datasets are heterogeneous (quantified by certain
measurable constants), the communication rounds needed to achieve a targeted
accuracy are reduced thanks to the adaptive reuse of lagged gradients.
Numerical experiments on both synthetic and real data corroborate a significant
communication reduction compared to alternatives.
|
This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation.
|
http://arxiv.org/abs/1805.09965v2
|
http://arxiv.org/pdf/1805.09965v2.pdf
|
NeurIPS 2018 12
|
[
"Tianyi Chen",
"Georgios B. Giannakis",
"Tao Sun",
"Wotao Yin"
] |
[] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/7752-lag-lazily-aggregated-gradient-for-communication-efficient-distributed-learning
|
http://papers.nips.cc/paper/7752-lag-lazily-aggregated-gradient-for-communication-efficient-distributed-learning.pdf
|
lag-lazily-aggregated-gradient-for-1
| null |
[] |
https://paperswithcode.com/paper/anaphora-and-coreference-resolution-a-review
|
1805.11824
| null | null |
Anaphora and Coreference Resolution: A Review
|
Entity resolution aims at resolving repeated references to an entity in a
document and forms a core component of natural language processing (NLP)
research. This field possesses immense potential to improve the performance of
other NLP fields like machine translation, sentiment analysis, paraphrase
detection, summarization, etc. The area of entity resolution in NLP has seen
proliferation of research in two separate sub-areas namely: anaphora resolution
and coreference resolution. Through this review article, we aim at clarifying
the scope of these two tasks in entity resolution. We also carry out a detailed
analysis of the datasets, evaluation metrics and research methods that have
been adopted to tackle this NLP problem. This survey is motivated with the aim
of providing the reader with a clear understanding of what constitutes this NLP
problem and the issues that require attention.
| null |
http://arxiv.org/abs/1805.11824v1
|
http://arxiv.org/pdf/1805.11824v1.pdf
| null |
[
"Rhea Sukthanker",
"Soujanya Poria",
"Erik Cambria",
"Ramkumar Thirunavukarasu"
] |
[
"coreference-resolution",
"Coreference Resolution",
"Entity Resolution",
"Machine Translation",
"Sentiment Analysis",
"Translation"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generic-cp-supported-cmsa-for-binary-integer
|
1805.11820
| null | null |
Generic CP-Supported CMSA for Binary Integer Linear Programs
|
Construct, Merge, Solve and Adapt (CMSA) is a general hybrid metaheuristic
for solving combinatorial optimization problems. At each iteration, CMSA (1)
constructs feasible solutions to the tackled problem instance in a
probabilistic way and (2) solves a reduced problem instance (if possible) to
optimality. The construction of feasible solutions is hereby problem-specific,
usually involving a fast greedy heuristic. The goal of this paper is to design
a problem-agnostic CMSA variant whose exclusive input is an integer linear
program (ILP). In order to reduce the complexity of this task, the current
study is restricted to binary ILPs. In addition to a basic problem-agnostic
CMSA variant, we also present an extended version that makes use of a
constraint propagation engine for constructing solutions. The results show that
our technique is able to match the upper bounds of the standalone application
of CPLEX in the context of rather easy-to-solve instances, while it generally
outperforms the standalone application of CPLEX in the context of hard
instances. Moreover, the results indicate that the support of the constraint
propagation engine is useful in the context of problems for which finding
feasible solutions is rather difficult.
| null |
http://arxiv.org/abs/1805.11820v1
|
http://arxiv.org/pdf/1805.11820v1.pdf
| null |
[
"Christian Blum",
"Haroldo Gambini Santos"
] |
[
"Combinatorial Optimization"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/visual-referring-expression-recognition-what
|
1805.11818
| null | null |
Visual Referring Expression Recognition: What Do Systems Actually Learn?
|
We present an empirical analysis of the state-of-the-art systems for
referring expression recognition -- the task of identifying the object in an
image referred to by a natural language expression -- with the goal of gaining
insight into how these systems reason about language and vision. Surprisingly,
we find strong evidence that even sophisticated and linguistically-motivated
models for this task may ignore the linguistic structure, instead relying on
shallow correlations introduced by unintended biases in the data selection and
annotation process. For example, we show that a system trained and tested on
the input image $\textit{without the input referring expression}$ can achieve a
precision of 71.2% in top-2 predictions. Furthermore, a system that predicts
only the object category given the input can achieve a precision of 84.2% in
top-2 predictions. These surprisingly positive results for what should be
deficient prediction scenarios suggest that careful analysis of what our models
are learning -- and further, how our data is constructed -- is critical as we
seek to make substantive progress on grounded language tasks.
|
We present an empirical analysis of the state-of-the-art systems for referring expression recognition -- the task of identifying the object in an image referred to by a natural language expression -- with the goal of gaining insight into how these systems reason about language and vision.
|
http://arxiv.org/abs/1805.11818v1
|
http://arxiv.org/pdf/1805.11818v1.pdf
|
NAACL 2018 6
|
[
"Volkan Cirik",
"Louis-Philippe Morency",
"Taylor Berg-Kirkpatrick"
] |
[
"Referring Expression"
] | 2018-05-30T00:00:00 |
https://aclanthology.org/N18-2123
|
https://aclanthology.org/N18-2123.pdf
|
visual-referring-expression-recognition-what-1
| null |
[] |
https://paperswithcode.com/paper/enabling-pedestrian-safety-using-computer
|
1805.11815
| null | null |
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
|
Human lives are important. The decision to allow self-driving vehicles
operate on our roads carries great weight. This has been a hot topic of debate
between policy-makers, technologists and public safety institutions. The recent
Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has
strengthened the argument that autonomous vehicle technology is still not ready
for deployment on public roads. In this work, we analyze the Uber car crash and
shed light on the question, "Could the Uber Car Crash have been avoided?". We
apply state-of-the-art Computer Vision models to this highly practical
scenario. More generally, our experimental results are an evaluation of various
image enhancement and object recognition techniques for enabling pedestrian
safety in low-lighting conditions using the Uber crash as a case study.
| null |
http://arxiv.org/abs/1805.11815v1
|
http://arxiv.org/pdf/1805.11815v1.pdf
| null |
[
"Puneet Kohli",
"Anjali Chadha"
] |
[
"Image Enhancement",
"Object Recognition"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-progressive-batching-l-bfgs-method-for
|
1802.05374
| null | null |
A Progressive Batching L-BFGS Method for Machine Learning
|
The standard L-BFGS method relies on gradient approximations that are not
dominated by noise, so that search directions are descent directions, the line
search is reliable, and quasi-Newton updating yields useful quadratic models of
the objective function. All of this appears to call for a full batch approach,
but since small batch sizes give rise to faster algorithms with better
generalization properties, L-BFGS is currently not considered an algorithm of
choice for large-scale machine learning applications. One need not, however,
choose between the two extremes represented by the full batch or highly
stochastic regimes, and may instead follow a progressive batching approach in
which the sample size increases during the course of the optimization. In this
paper, we present a new version of the L-BFGS algorithm that combines three
basic components - progressive batching, a stochastic line search, and stable
quasi-Newton updating - and that performs well on training logistic regression
and deep neural networks. We provide supporting convergence theory for the
method.
| null |
http://arxiv.org/abs/1802.05374v2
|
http://arxiv.org/pdf/1802.05374v2.pdf
|
ICML 2018 7
|
[
"Raghu Bollapragada",
"Dheevatsa Mudigere",
"Jorge Nocedal",
"Hao-Jun Michael Shi",
"Ping Tak Peter Tang"
] |
[
"BIG-bench Machine Learning"
] | 2018-02-15T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2059
|
http://proceedings.mlr.press/v80/bollapragada18a/bollapragada18a.pdf
|
a-progressive-batching-l-bfgs-method-for-1
| null |
[] |
https://paperswithcode.com/paper/crrn-multi-scale-guided-concurrent-reflection
|
1805.11802
| null | null |
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
|
Removing the undesired reflections from images taken through the glass is of
broad application to various computer vision tasks. Non-learning based methods
utilize different handcrafted priors such as the separable sparse gradients
caused by different levels of blurs, which often fail due to their limited
description capability to the properties of real-world reflections. In this
paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle
this problem in a unified framework. Our proposed network integrates image
appearance information and multi-scale gradient information with human
perception inspired loss function, and is trained on a new dataset with 3250
reflection images taken under diverse real-world scenes. Extensive experiments
on a public benchmark dataset show that the proposed method performs favorably
against state-of-the-art methods.
|
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks.
|
http://arxiv.org/abs/1805.11802v1
|
http://arxiv.org/pdf/1805.11802v1.pdf
|
CVPR 2018 6
|
[
"Renjie Wan",
"Boxin Shi",
"Ling-Yu Duan",
"Ah-Hwee Tan",
"Alex C. Kot"
] |
[
"Reflection Removal"
] | 2018-05-30T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Wan_CRRN_Multi-Scale_Guided_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Wan_CRRN_Multi-Scale_Guided_CVPR_2018_paper.pdf
|
crrn-multi-scale-guided-concurrent-reflection-1
| null |
[] |
https://paperswithcode.com/paper/automated-proof-synthesis-for-propositional
|
1805.11799
| null | null |
Automated proof synthesis for propositional logic with deep neural networks
|
This work explores the application of deep learning, a machine learning
technique that uses deep neural networks (DNN) in its core, to an automated
theorem proving (ATP) problem. To this end, we construct a statistical model
which quantifies the likelihood that a proof is indeed a correct one of a given
proposition. Based on this model, we give a proof-synthesis procedure that
searches for a proof in the order of the likelihood. This procedure uses an
estimator of the likelihood of an inference rule being applied at each step of
a proof. As an implementation of the estimator, we propose a
proposition-to-proof architecture, which is a DNN tailored to the automated
proof synthesis problem. To empirically demonstrate its usefulness, we apply
our model to synthesize proofs of propositional logic. We train the
proposition-to-proof model using a training dataset of proposition-proof pairs.
The evaluation against a benchmark set shows the very high accuracy and an
improvement to the recent work of neural proof synthesis.
|
As an implementation of the estimator, we propose a proposition-to-proof architecture, which is a DNN tailored to the automated proof synthesis problem.
|
http://arxiv.org/abs/1805.11799v1
|
http://arxiv.org/pdf/1805.11799v1.pdf
| null |
[
"Taro Sekiyama",
"Kohei Suenaga"
] |
[
"Automated Theorem Proving"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dialogue-modeling-via-hash-functions
|
1804.10188
| null | null |
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
|
We propose a novel dialogue modeling framework, the first-ever nonparametric kernel functions based approach for dialogue modeling, which learns kernelized hashcodes as compressed text representations; unlike traditional deep learning models, it handles well relatively small datasets, while also scaling to large ones. We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses. As demonstrated on three real-life datasets, including prominently psychotherapy sessions, the proposed approach significantly outperforms several state-of-art neural network based dialogue systems, both in terms of computational efficiency, reducing training time from days or weeks to hours, and the response quality, achieving an order of magnitude improvement over competitors in frequency of being chosen as the best model by human evaluators.
| null |
https://arxiv.org/abs/1804.10188v7
|
https://arxiv.org/pdf/1804.10188v7.pdf
| null |
[
"Sahil Garg",
"Irina Rish",
"Guillermo Cecchi",
"Palash Goyal",
"Sarik Ghazarian",
"Shuyang Gao",
"Greg Ver Steeg",
"Aram Galstyan"
] |
[
"Computational Efficiency",
"Dialogue Generation",
"Model Selection"
] | 2018-04-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/infinite-arms-bandit-optimality-via
|
1805.11793
| null | null |
Infinite Arms Bandit: Optimality via Confidence Bounds
|
Berry et al. (1997) initiated the development of the infinite arms bandit problem. They derived a regret lower bound of all allocation strategies for Bernoulli rewards with uniform priors, and proposed strategies based on success runs. Bonald and Prouti\`{e}re (2013) proposed a two-target algorithm that achieves the regret lower bound, and extended optimality to Bernoulli rewards with general priors. We present here a confidence bound target (CBT) algorithm that achieves optimality for rewards that are bounded above. For each arm we construct a confidence bound and compare it against each other and a target value to determine if the arm should be sampled further. The target value depends on the assumed priors of the arm means. In the absence of information on the prior, the target value is determined empirically. Numerical studies here show that CBT is versatile and outperforms its competitors.
| null |
https://arxiv.org/abs/1805.11793v4
|
https://arxiv.org/pdf/1805.11793v4.pdf
| null |
[
"Hock Peng Chan",
"Shouri Hu"
] |
[] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tight-regret-bounds-for-bayesian-optimization
|
1805.11792
| null | null |
Tight Regret Bounds for Bayesian Optimization in One Dimension
|
We consider the problem of Bayesian optimization (BO) in one dimension, under a Gaussian process prior and Gaussian sampling noise. We provide a theoretical analysis showing that, under fairly mild technical assumptions on the kernel, the best possible cumulative regret up to time $T$ behaves as $\Omega(\sqrt{T})$ and $O(\sqrt{T\log T})$. This gives a tight characterization up to a $\sqrt{\log T}$ factor, and includes the first non-trivial lower bound for noisy BO. Our assumptions are satisfied, for example, by the squared exponential and Mat\'ern-$\nu$ kernels, with the latter requiring $\nu > 2$. Our results certify the near-optimality of existing bounds (Srinivas {\em et al.}, 2009) for the SE kernel, while proving them to be strictly suboptimal for the Mat\'ern kernel with $\nu > 2$.
| null |
https://arxiv.org/abs/1805.11792v3
|
https://arxiv.org/pdf/1805.11792v3.pdf
|
ICML 2018 7
|
[
"Jonathan Scarlett"
] |
[
"Bayesian Optimization"
] | 2018-05-30T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=1945
|
http://proceedings.mlr.press/v80/scarlett18a/scarlett18a.pdf
|
tight-regret-bounds-for-bayesian-optimization-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/a-hierarchical-end-to-end-model-for-jointly
|
1805.01089
| null | null |
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
|
Text summarization and sentiment classification both aim to capture the main
ideas of the text but at different levels. Text summarization is to describe
the text within a few sentences, while sentiment classification can be regarded
as a special type of summarization which "summarizes" the text into a even more
abstract fashion, i.e., a sentiment class. Based on this idea, we propose a
hierarchical end-to-end model for joint learning of text summarization and
sentiment classification, where the sentiment classification label is treated
as the further "summarization" of the text summarization output. Hence, the
sentiment classification layer is put upon the text summarization layer, and a
hierarchical structure is derived. Experimental results on Amazon online
reviews datasets show that our model achieves better performance than the
strong baseline systems on both abstractive summarization and sentiment
classification.
| null |
http://arxiv.org/abs/1805.01089v2
|
http://arxiv.org/pdf/1805.01089v2.pdf
| null |
[
"Shuming Ma",
"Xu sun",
"Junyang Lin",
"Xuancheng Ren"
] |
[
"Abstractive Text Summarization",
"Classification",
"General Classification",
"Sentiment Analysis",
"Sentiment Classification",
"Text Summarization"
] | 2018-05-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-fine-to-coarse-convolutional-neural-network
|
1805.11790
| null | null |
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
|
This paper presents a new framework for human action recognition from a 3D
skeleton sequence. Previous studies do not fully utilize the temporal
relationships between video segments in a human action. Some studies
successfully used very deep Convolutional Neural Network (CNN) models but often
suffer from the data insufficiency problem. In this study, we first segment a
skeleton sequence into distinct temporal segments in order to exploit the
correlations between them. The temporal and spatial features of a skeleton
sequence are then extracted simultaneously by utilizing a fine-to-coarse (F2C)
CNN architecture optimized for human skeleton sequences. We evaluate our
proposed method on NTU RGB+D and SBU Kinect Interaction dataset. It achieves
79.6% and 84.6% of accuracies on NTU RGB+D with cross-object and cross-view
protocol, respectively, which are almost identical with the state-of-the-art
performance. In addition, our method significantly improves the accuracy of the
actions in two-person interactions.
| null |
http://arxiv.org/abs/1805.11790v2
|
http://arxiv.org/pdf/1805.11790v2.pdf
| null |
[
"Thao Minh Le",
"Nakamasa Inoue",
"Koichi Shinoda"
] |
[
"3D Action Recognition",
"Action Recognition",
"Skeleton Based Action Recognition",
"Temporal Action Localization"
] | 2018-05-30T00:00:00 | null | null | null | null |
[] |
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