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classes | framework
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|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/heterogeneity-and-instability-in-the-stable
|
Heterogeneity and Instability in the Stable Marriage Problem
|
1902.09226
|
http://arxiv.org/abs/1902.09226v1
|
http://arxiv.org/pdf/1902.09226v1.pdf
|
https://github.com/BAFurtado/HISMP
| true | true | true |
none
|
https://paperswithcode.com/paper/attention-based-recurrent-neural-network
|
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
|
1609.01454
|
http://arxiv.org/abs/1609.01454v1
|
http://arxiv.org/pdf/1609.01454v1.pdf
|
https://github.com/pengshuang/Joint-Slot-Filling
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-semantics-for-visual-place
|
Learning Semantics for Visual Place Recognition through Multi-Scale Attention
|
2201.09701
|
https://arxiv.org/abs/2201.09701v2
|
https://arxiv.org/pdf/2201.09701v2.pdf
|
https://github.com/valeriopaolicelli/SegVPR
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/sumnet-fully-convolutional-model-for-fast
|
SUMNet: Fully Convolutional Model for Fast Segmentation of Anatomical Structures in Ultrasound Volumes
|
1901.06920
|
http://arxiv.org/abs/1901.06920v1
|
http://arxiv.org/pdf/1901.06920v1.pdf
|
https://github.com/drvelmuruganb/EDD2020-Endoscopy-disease-detection-grand-challenge-2020-
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/learning-symmetric-and-low-energy-locomotion
|
Learning Symmetric and Low-energy Locomotion
|
1801.08093
|
http://arxiv.org/abs/1801.08093v3
|
http://arxiv.org/pdf/1801.08093v3.pdf
|
https://github.com/VincentYu68/SymmetryCurriculumLocomotion
| false | false | true |
none
|
https://paperswithcode.com/paper/improved-techniques-for-training-gans
|
Improved Techniques for Training GANs
|
1606.03498
|
http://arxiv.org/abs/1606.03498v1
|
http://arxiv.org/pdf/1606.03498v1.pdf
|
https://github.com/chameleonTK/continual-learning-for-HAR
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-deep-reinforced-model-for-abstractive
|
A Deep Reinforced Model for Abstractive Summarization
|
1705.04304
|
http://arxiv.org/abs/1705.04304v3
|
http://arxiv.org/pdf/1705.04304v3.pdf
|
https://github.com/AndreyKolomiets/News_Headline_Generation
| false | false | true |
tf
|
https://paperswithcode.com/paper/dual-signal-transformation-lstm-network-for
|
Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression
|
2005.07551
|
https://arxiv.org/abs/2005.07551v1
|
https://arxiv.org/pdf/2005.07551v1.pdf
|
https://github.com/breizhn/DTLN
| true | true | true |
tf
|
https://paperswithcode.com/paper/fastdvdnet-towards-real-time-video-denoising
|
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation
|
1907.01361
|
https://arxiv.org/abs/1907.01361v2
|
https://arxiv.org/pdf/1907.01361v2.pdf
|
https://github.com/samyakjain0112/Video-denoising_tensorflow-core-api-2.0
| false | false | true |
tf
|
https://paperswithcode.com/paper/compression-of-deep-convolutional-neural
|
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
|
1511.06530
|
http://arxiv.org/abs/1511.06530v2
|
http://arxiv.org/pdf/1511.06530v2.pdf
|
https://github.com/jacobgil/pytorch-tensor-decompositions
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/theory-and-computation-of-electromagnetic
|
Theory and computation of electromagnetic fields and thermomechanical structure interaction for systems undergoing large deformations
|
1803.10551
|
http://arxiv.org/abs/1803.10551v1
|
http://arxiv.org/pdf/1803.10551v1.pdf
|
https://github.com/afqueiruga/EMSI-2018
| true | true | true |
none
|
https://paperswithcode.com/paper/key-value-retrieval-networks-for-task
|
Key-Value Retrieval Networks for Task-Oriented Dialogue
|
1705.05414
|
http://arxiv.org/abs/1705.05414v2
|
http://arxiv.org/pdf/1705.05414v2.pdf
|
https://github.com/garygsw/Nav-NNDial
| false | false | true |
none
|
https://paperswithcode.com/paper/squad-100000-questions-for-machine
|
SQuAD: 100,000+ Questions for Machine Comprehension of Text
|
1606.05250
|
http://arxiv.org/abs/1606.05250v3
|
http://arxiv.org/pdf/1606.05250v3.pdf
|
https://github.com/chrisc36/debias
| false | false | true |
tf
|
https://paperswithcode.com/paper/variational-reformulation-of-bayesian-inverse
|
Variational Reformulation of Bayesian Inverse Problems
|
1410.5522
|
http://arxiv.org/abs/1410.5522v1
|
http://arxiv.org/pdf/1410.5522v1.pdf
|
https://github.com/KhurramPirov/bayesian_Inverse
| false | false | true |
none
|
https://paperswithcode.com/paper/non-stationary-bandits-and-meta-learning-with
|
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
|
2202.13001
|
https://arxiv.org/abs/2202.13001v6
|
https://arxiv.org/pdf/2202.13001v6.pdf
|
https://github.com/duongnhatthang/meta-bandit
| true | false | true |
none
|
https://paperswithcode.com/paper/implicit-quantile-networks-for-distributional
|
Implicit Quantile Networks for Distributional Reinforcement Learning
|
1806.06923
|
http://arxiv.org/abs/1806.06923v1
|
http://arxiv.org/pdf/1806.06923v1.pdf
|
https://github.com/V0LsTeR/DQN_heap
| false | false | true |
tf
|
https://paperswithcode.com/paper/simple-random-search-provides-a-competitive
|
Simple random search provides a competitive approach to reinforcement learning
|
1803.07055
|
http://arxiv.org/abs/1803.07055v1
|
http://arxiv.org/pdf/1803.07055v1.pdf
|
https://github.com/AnshMittal1811/AugmentedRandomSearch
| false | false | true |
none
|
https://paperswithcode.com/paper/reinforcement-learning-upside-down-dont
|
Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions
|
1912.02875
|
https://arxiv.org/abs/1912.02875v2
|
https://arxiv.org/pdf/1912.02875v2.pdf
|
https://github.com/haron1100/Upside-Down-Reinforcement-Learning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/robust-quantum-entanglement-generation-and
|
Robust Quantum Entanglement Generation and Generation-plus-Storage Protocols with Spin Chains
|
1612.05097
|
http://arxiv.org/abs/1612.05097v2
|
http://arxiv.org/pdf/1612.05097v2.pdf
|
https://github.com/estaremp/spinchain
| false | false | true |
none
|
https://paperswithcode.com/paper/modular-vehicle-control-for-transferring
|
Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs
|
1807.01001
|
http://arxiv.org/abs/1807.01001v2
|
http://arxiv.org/pdf/1807.01001v2.pdf
|
https://github.com/pmwenzel/carla-domain-adaptation
| false | false | true |
none
|
https://paperswithcode.com/paper/estimating-causal-effects-in-the-presence-of
|
Estimating the effectiveness of permanent price reductions for competing products using multivariate Bayesian structural time series models
|
2006.12269
|
https://arxiv.org/abs/2006.12269v4
|
https://arxiv.org/pdf/2006.12269v4.pdf
|
https://github.com/FMenchetti/CausalMBSTS
| false | false | true |
none
|
https://paperswithcode.com/paper/higan-cosmic-neutral-hydrogen-with-generative
|
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
|
1904.12846
|
http://arxiv.org/abs/1904.12846v1
|
http://arxiv.org/pdf/1904.12846v1.pdf
|
https://github.com/jjzamudio/HIGAN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/multi-view-low-rank-sparse-subspace
|
Multi-view Low-rank Sparse Subspace Clustering
|
1708.08732
|
http://arxiv.org/abs/1708.08732v1
|
http://arxiv.org/pdf/1708.08732v1.pdf
|
https://github.com/mbrbic/Multi-view-LRSSC
| false | false | true |
none
|
https://paperswithcode.com/paper/rsa-byzantine-robust-stochastic-aggregation
|
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
|
1811.03761
|
https://arxiv.org/abs/1811.03761v2
|
https://arxiv.org/pdf/1811.03761v2.pdf
|
https://github.com/Liepill/RSA-Byzantine
| true | true | true |
none
|
https://paperswithcode.com/paper/neural-spline-flows
|
Neural Spline Flows
|
1906.04032
|
https://arxiv.org/abs/1906.04032v2
|
https://arxiv.org/pdf/1906.04032v2.pdf
|
https://github.com/johnpjust/nsf
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/speeding-up-convolutional-neural-networks
|
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
|
1412.6553
|
http://arxiv.org/abs/1412.6553v3
|
http://arxiv.org/pdf/1412.6553v3.pdf
|
https://github.com/jacobgil/pytorch-tensor-decompositions
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/generating-adversarial-examples-with
|
Generating Adversarial Examples with Adversarial Networks
|
1801.02610
|
http://arxiv.org/abs/1801.02610v5
|
http://arxiv.org/pdf/1801.02610v5.pdf
|
https://github.com/abahram77/mnistChallenge
| false | false | true |
tf
|
https://paperswithcode.com/paper/the-cot-collection-improving-zero-shot-and
|
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
|
2305.14045
|
https://arxiv.org/abs/2305.14045v2
|
https://arxiv.org/pdf/2305.14045v2.pdf
|
https://github.com/kaistai/cot-collection
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/mid-flight-propeller-failure-detection-and
|
Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning
|
2002.11564
|
https://arxiv.org/abs/2002.11564v2
|
https://arxiv.org/pdf/2002.11564v2.pdf
|
https://github.com/Aakriti05/Prop-Fail-Detect-Control-RL
| true | true | true |
tf
|
https://paperswithcode.com/paper/quantum-equilibrium-disequilibrium-asset
|
"Quantum Equilibrium-Disequilibrium": Asset Price Dynamics, Symmetry Breaking, and Defaults as Dissipative Instantons
|
1808.03607
|
http://arxiv.org/abs/1808.03607v2
|
http://arxiv.org/pdf/1808.03607v2.pdf
|
https://github.com/gowen100/Machine-Learning
| false | false | true |
tf
|
https://paperswithcode.com/paper/active-learning-for-decision-making-from
|
Active Learning for Decision-Making from Imbalanced Observational Data
|
1904.05268
|
https://arxiv.org/abs/1904.05268v2
|
https://arxiv.org/pdf/1904.05268v2.pdf
|
https://github.com/IirisSundin/active-learning-for-decision-making
| false | false | true |
none
|
https://paperswithcode.com/paper/deep-learning-with-differential-privacy
|
Deep Learning with Differential Privacy
|
1607.00133
|
http://arxiv.org/abs/1607.00133v2
|
http://arxiv.org/pdf/1607.00133v2.pdf
|
https://github.com/sunblaze-ucb/dpml-benchmark
| false | false | true |
tf
|
https://paperswithcode.com/paper/graph-attention-networks
|
Graph Attention Networks
|
1710.10903
|
http://arxiv.org/abs/1710.10903v3
|
http://arxiv.org/pdf/1710.10903v3.pdf
|
https://github.com/subercui/pyGConvAT
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/interpretability-beyond-feature-attribution
|
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
|
1711.11279
|
http://arxiv.org/abs/1711.11279v5
|
http://arxiv.org/pdf/1711.11279v5.pdf
|
https://github.com/pytorch/captum
| false | false | false |
pytorch
|
https://paperswithcode.com/paper/variance-reduction-in-sgd-by-distributed
|
Variance Reduction in SGD by Distributed Importance Sampling
|
1511.06481
|
http://arxiv.org/abs/1511.06481v7
|
http://arxiv.org/pdf/1511.06481v7.pdf
|
https://github.com/idiap/importance-sampling
| false | false | true |
tf
|
https://paperswithcode.com/paper/text-classification-with-word-embedding
|
Text classification with word embedding regularization and soft similarity measure
|
2003.05019
|
https://arxiv.org/abs/2003.05019v1
|
https://arxiv.org/pdf/2003.05019v1.pdf
|
https://github.com/MIR-MU/regularized-embeddings
| true | true | true |
none
|
https://paperswithcode.com/paper/osvidcap-a-framework-for-the-simultaneous
|
OSVidCap: A Framework for the Simultaneous Recognition and Description of Concurrent Actions in Videos in an Open-Set Scenario
| null |
https://ieeexplore.ieee.org/abstract/document/9552885
|
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552885
|
https://github.com/bioinfolabic/OSVidCap
| true | false | false |
none
|
https://paperswithcode.com/paper/fully-convolutional-networks-for-semantic-1
|
Fully Convolutional Networks for Semantic Segmentation
|
1411.4038
|
http://arxiv.org/abs/1411.4038v2
|
http://arxiv.org/pdf/1411.4038v2.pdf
|
https://github.com/SethEBaldwin/FCN
| false | false | true |
tf
|
https://paperswithcode.com/paper/multi-agent-actor-critic-for-mixed
|
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
|
1706.02275
|
https://arxiv.org/abs/1706.02275v4
|
https://arxiv.org/pdf/1706.02275v4.pdf
|
https://github.com/thechrisyoon08/marl
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/inductive-document-network-embedding-with
|
Inductive Document Network Embedding with Topic-Word Attention
|
2001.03369
|
https://arxiv.org/abs/2001.03369v1
|
https://arxiv.org/pdf/2001.03369v1.pdf
|
https://github.com/brochier/idne
| true | true | true |
none
|
https://paperswithcode.com/paper/generation-of-point-sets-by-convex
|
Generation of point sets by convex optimization for interpolation in reproducing kernel Hilbert spaces
|
1810.08505
|
http://arxiv.org/abs/1810.08505v1
|
http://arxiv.org/pdf/1810.08505v1.pdf
|
https://github.com/KeTanakaN/mat_points_interp_rkhs
| true | true | true |
none
|
https://paperswithcode.com/paper/sq-vae-variational-bayes-on-discrete
|
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
|
2205.07547
|
https://arxiv.org/abs/2205.07547v2
|
https://arxiv.org/pdf/2205.07547v2.pdf
|
https://github.com/sony/sqvae
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/improved-lower-bounds-for-queen-s-domination
|
Improved lower bounds for Queen's Domination via an exactly-solvable relaxation
|
2304.06620
|
https://arxiv.org/abs/2304.06620v1
|
https://arxiv.org/pdf/2304.06620v1.pdf
|
https://github.com/architkarandikar/queens-domination
| true | true | false |
none
|
https://paperswithcode.com/paper/hierarchical-reinforcement-learning-with-5
|
Hierarchical Reinforcement Learning with Timed Subgoals
|
2112.03100
|
https://arxiv.org/abs/2112.03100v1
|
https://arxiv.org/pdf/2112.03100v1.pdf
|
https://github.com/martius-lab/hits
| true | true | false |
none
|
https://paperswithcode.com/paper/convolutional-neural-networks-for-sentence
|
Convolutional Neural Networks for Sentence Classification
|
1408.5882
|
http://arxiv.org/abs/1408.5882v2
|
http://arxiv.org/pdf/1408.5882v2.pdf
|
https://github.com/paper-cat/Sentence-Classifications
| false | false | true |
tf
|
https://paperswithcode.com/paper/mimics-a-large-scale-data-collection-for
|
MIMICS: A Large-Scale Data Collection for Search Clarification
|
2006.10174
|
https://arxiv.org/abs/2006.10174v1
|
https://arxiv.org/pdf/2006.10174v1.pdf
|
https://github.com/microsoft/MIMICS
| true | true | true |
none
|
https://paperswithcode.com/paper/a-low-cost-cryogenic-temperature-measurement
|
A low-cost cryogenic temperature measurement system using Arduino microcontroller
|
1910.09111
|
https://arxiv.org/abs/1910.09111v1
|
https://arxiv.org/pdf/1910.09111v1.pdf
|
https://github.com/kinetic-orange/Low-Cost-Cryogenic-Temperature-Measurement-System
| false | false | true |
none
|
https://paperswithcode.com/paper/betrayed-by-captions-joint-caption-grounding
|
Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation
|
2301.00805
|
https://arxiv.org/abs/2301.00805v2
|
https://arxiv.org/pdf/2301.00805v2.pdf
|
https://github.com/jianzongwu/betrayed-by-captions
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/continuous-control-with-deep-reinforcement
|
Continuous control with deep reinforcement learning
|
1509.02971
|
https://arxiv.org/abs/1509.02971v6
|
https://arxiv.org/pdf/1509.02971v6.pdf
|
https://github.com/susan-amin/SparseBaseline1
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/parameter-space-noise-for-exploration
|
Parameter Space Noise for Exploration
|
1706.01905
|
http://arxiv.org/abs/1706.01905v2
|
http://arxiv.org/pdf/1706.01905v2.pdf
|
https://github.com/susan-amin/SparseBaseline1
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/soft-actor-critic-off-policy-maximum-entropy
|
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
|
1801.01290
|
http://arxiv.org/abs/1801.01290v2
|
http://arxiv.org/pdf/1801.01290v2.pdf
|
https://github.com/susan-amin/SparseBaseline1
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-graph-enhanced-click-model-for-web-search-1
|
A Graph-Enhanced Click Model for Web Search
|
2206.08621
|
https://arxiv.org/abs/2206.08621v2
|
https://arxiv.org/pdf/2206.08621v2.pdf
|
https://github.com/chiangel/graphcm
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/distilling-the-knowledge-in-a-neural-network
|
Distilling the Knowledge in a Neural Network
|
1503.02531
|
http://arxiv.org/abs/1503.02531v1
|
http://arxiv.org/pdf/1503.02531v1.pdf
|
https://github.com/robertjkeck2/EmoNet
| false | false | true |
none
|
https://paperswithcode.com/paper/transferability-of-natural-language-inference
|
Transferability of Natural Language Inference to Biomedical Question Answering
|
2007.00217
|
https://arxiv.org/abs/2007.00217v4
|
https://arxiv.org/pdf/2007.00217v4.pdf
|
https://github.com/dmis-lab/bioasq8b
| true | true | true |
tf
|
https://paperswithcode.com/paper/deep-learning-to-represent-sub-grid-processes
|
Deep learning to represent sub-grid processes in climate models
|
1806.04731
|
http://arxiv.org/abs/1806.04731v3
|
http://arxiv.org/pdf/1806.04731v3.pdf
|
https://github.com/jordanott/CBRAIN-CAM
| false | false | true |
tf
|
https://paperswithcode.com/paper/hololens-2-research-mode-as-a-tool-for
|
HoloLens 2 Research Mode as a Tool for Computer Vision Research
|
2008.11239
|
https://arxiv.org/abs/2008.11239v1
|
https://arxiv.org/pdf/2008.11239v1.pdf
|
https://github.com/microsoft/HoloLens2ForCV
| true | true | true |
none
|
https://paperswithcode.com/paper/mcnntunes-tuning-shower-monte-carlo
|
MCNNTUNES: tuning Shower Monte Carlo generators with machine learning
|
2010.02213
|
https://arxiv.org/abs/2010.02213v1
|
https://arxiv.org/pdf/2010.02213v1.pdf
|
https://github.com/N3PDF/mcnntunes
| true | true | true |
tf
|
https://paperswithcode.com/paper/counterfactual-variable-control-for-robust
|
Counterfactual Variable Control for Robust and Interpretable Question Answering
|
2010.05581
|
https://arxiv.org/abs/2010.05581v1
|
https://arxiv.org/pdf/2010.05581v1.pdf
|
https://github.com/PluviophileYU/CVC-QA
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/unsuperpoint-end-to-end-unsupervised-interest
|
UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor
|
1907.04011
|
https://arxiv.org/abs/1907.04011v1
|
https://arxiv.org/pdf/1907.04011v1.pdf
|
https://github.com/kimphys/UnsuperPoint.pytorch
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/grzegorczyk-sequence
|
Grzegorczyk sequence
|
1811.09958
|
https://arxiv.org/abs/1811.09958v1
|
https://arxiv.org/pdf/1811.09958v1.pdf
|
https://github.com/koteitan/grzeseq
| false | false | true |
none
|
https://paperswithcode.com/paper/gaussianizing-the-earth-multidimensional
|
Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis
|
2010.06476
|
https://arxiv.org/abs/2010.06476v2
|
https://arxiv.org/pdf/2010.06476v2.pdf
|
https://github.com/IPL-UV/rbig
| true | false | true |
none
|
https://paperswithcode.com/paper/neural-network-based-generation-of-1
|
Neural network based generation of a 1-dimensional stochastic field with turbulent velocity statistics
|
2211.11580
|
https://arxiv.org/abs/2211.11580v3
|
https://arxiv.org/pdf/2211.11580v3.pdf
|
https://github.com/cgranerob/nn-turb
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/adversarial-turing-patterns-from-cellular
|
Adversarial Turing Patterns from Cellular Automata
|
2011.09393
|
https://arxiv.org/abs/2011.09393v3
|
https://arxiv.org/pdf/2011.09393v3.pdf
|
https://github.com/NurislamT/advTuring
| true | true | true |
none
|
https://paperswithcode.com/paper/relating-reading-visualization-and-coding
|
Relating Reading, Visualization, and Coding forNew Programmers: A Neuroimaging Study
|
2102.12376
|
https://arxiv.org/abs/2102.12376v1
|
https://arxiv.org/pdf/2102.12376v1.pdf
|
https://github.com/CelloCorgi/ICSE_fNIRS2021
| true | true | true |
none
|
https://paperswithcode.com/paper/synchronous-counting-and-computational
|
Synchronous Counting and Computational Algorithm Design
|
1304.5719
|
https://arxiv.org/abs/1304.5719v2
|
https://arxiv.org/pdf/1304.5719v2.pdf
|
https://github.com/suomela/counting
| true | true | true |
none
|
https://paperswithcode.com/paper/self-critical-sequence-training-for-image
|
Self-critical Sequence Training for Image Captioning
|
1612.00563
|
http://arxiv.org/abs/1612.00563v2
|
http://arxiv.org/pdf/1612.00563v2.pdf
|
https://github.com/FJSam/SelfCritical_ImageCaptioning
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/katie-for-parton-level-event-generation-with
|
KaTie: for parton-level event generation with k_T-dependent initial states
|
1611.00680
|
http://arxiv.org/abs/1611.00680v3
|
http://arxiv.org/pdf/1611.00680v3.pdf
|
https://bitbucket.org/hameren/katie
| true | true | true |
none
|
https://paperswithcode.com/paper/searching-for-electromagnetic-counterpart-of
|
Searching for electromagnetic counterpart of LIGO gravitational waves in the Fermi GBM data with ADWO
|
1603.06611
|
http://arxiv.org/abs/1603.06611v2
|
http://arxiv.org/pdf/1603.06611v2.pdf
|
https://github.com/zbagoly/ADWO
| true | true | true |
none
|
https://paperswithcode.com/paper/asymptotics-for-the-late-arrivals-problem
|
Asymptotics for the Late Arrivals Problem
|
1302.1999
|
http://arxiv.org/abs/1302.1999v6
|
http://arxiv.org/pdf/1302.1999v6.pdf
|
https://github.com/clancia/EDA
| true | true | true |
none
|
https://paperswithcode.com/paper/robust-watermarking-of-neural-network-with
|
Robust Watermarking of Neural Network with Exponential Weighting
|
1901.06151
|
http://arxiv.org/abs/1901.06151v1
|
http://arxiv.org/pdf/1901.06151v1.pdf
|
https://github.com/dunky11/exponential-weighting-watermarking
| false | false | true |
tf
|
https://paperswithcode.com/paper/deeppicker-a-deep-learning-approach-for-fully
|
DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
|
1605.01838
|
http://arxiv.org/abs/1605.01838v1
|
http://arxiv.org/pdf/1605.01838v1.pdf
|
https://github.com/nejyeah/DeepPicker-python
| false | false | true |
tf
|
https://paperswithcode.com/paper/global-optimality-in-model-predictive-control
|
Global optimality in model predictive control via hidden invariant convexity
|
2007.07062
|
https://arxiv.org/abs/2007.07062v2
|
https://arxiv.org/pdf/2007.07062v2.pdf
|
https://github.com/jbaayen/homotopy-example
| false | false | true |
none
|
https://paperswithcode.com/paper/combinatorial-reductions-for-the-stanley
|
Combinatorial Reductions for the Stanley Depth of $I$ and $S/I$
|
1702.00781
|
http://arxiv.org/abs/1702.00781v3
|
http://arxiv.org/pdf/1702.00781v3.pdf
|
https://github.com/mitchkeller/stanley-depth
| true | true | true |
none
|
https://paperswithcode.com/paper/real-time-machine-learning-the-missing-pieces
|
Real-Time Machine Learning: The Missing Pieces
|
1703.03924
|
http://arxiv.org/abs/1703.03924v2
|
http://arxiv.org/pdf/1703.03924v2.pdf
|
https://github.com/AmeerHajAli/ray2
| false | false | true |
tf
|
https://paperswithcode.com/paper/tune-a-research-platform-for-distributed
|
Tune: A Research Platform for Distributed Model Selection and Training
|
1807.05118
|
http://arxiv.org/abs/1807.05118v1
|
http://arxiv.org/pdf/1807.05118v1.pdf
|
https://github.com/AmeerHajAli/ray2
| false | false | true |
tf
|
https://paperswithcode.com/paper/tf-locoformer-transformer-with-local-modeling
|
TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement
|
2408.03440
|
https://arxiv.org/abs/2408.03440v1
|
https://arxiv.org/pdf/2408.03440v1.pdf
|
https://github.com/merlresearch/tf-locoformer
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/word-alignment-by-fine-tuning-embeddings-on
|
Word Alignment by Fine-tuning Embeddings on Parallel Corpora
|
2101.08231
|
https://arxiv.org/abs/2101.08231v4
|
https://arxiv.org/pdf/2101.08231v4.pdf
|
https://github.com/BramVanroy/astred
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/functional-central-limit-theorems-for-rough
|
Functional central limit theorems for rough volatility
|
1711.03078
|
https://arxiv.org/abs/1711.03078v4
|
https://arxiv.org/pdf/1711.03078v4.pdf
|
https://github.com/amuguruza/RoughFCLT
| true | true | true |
none
|
https://paperswithcode.com/paper/realtime-multi-person-2d-pose-estimation
|
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
|
1611.08050
|
http://arxiv.org/abs/1611.08050v2
|
http://arxiv.org/pdf/1611.08050v2.pdf
|
https://github.com/saadbutt32/Conversion-of-Pakistan-Sign-Languag-into-Text-and-Speech-using-OpenPose-and-Machine-Learning
| false | false | true |
none
|
https://paperswithcode.com/paper/preventing-extreme-polarization-of-political
|
Preventing Extreme Polarization of Political Attitudes
|
2103.06492
|
https://arxiv.org/abs/2103.06492v2
|
https://arxiv.org/pdf/2103.06492v2.pdf
|
https://github.com/jdaymude/AttractionRepulsionModel
| true | true | true |
none
|
https://paperswithcode.com/paper/fast-meningioma-segmentation-in-t1-weighted
|
Fast meningioma segmentation in T1-weighted MRI volumes using a lightweight 3D deep learning architecture
|
2010.07002
|
https://arxiv.org/abs/2010.07002v1
|
https://arxiv.org/pdf/2010.07002v1.pdf
|
https://github.com/andreped/PLS-Net
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/removing-word-level-spurious-alignment
|
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning
|
2104.13872
|
https://arxiv.org/abs/2104.13872v2
|
https://arxiv.org/pdf/2104.13872v2.pdf
|
https://github.com/ukyh/RemovingSpuriousAlignment
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/radioactive-data-tracing-through-training
|
Radioactive data: tracing through training
|
2002.00937
|
https://arxiv.org/abs/2002.00937v1
|
https://arxiv.org/pdf/2002.00937v1.pdf
|
https://github.com/facebookresearch/radioactive_data
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/pricing-and-energy-trading-in-peer-to-peer
|
Pricing and Energy Trading in Peer-to-peer Zero Marginal-cost Microgrids
|
2103.13530
|
https://arxiv.org/abs/2103.13530v4
|
https://arxiv.org/pdf/2103.13530v4.pdf
|
https://github.com/Energy-MAC/P2P-Pricing-Paper
| true | true | true |
none
|
https://paperswithcode.com/paper/network-clique-cover-approximation-to-analyze
|
Network clique cover approximation to analyze complex contagions through group interactions
|
2101.03618
|
https://arxiv.org/abs/2101.03618v3
|
https://arxiv.org/pdf/2101.03618v3.pdf
|
https://github.com/giubuig/DisjointCliqueCover.jl
| true | true | true |
none
|
https://paperswithcode.com/paper/controlling-for-unknown-confounders-in
|
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum
|
2006.13135
|
https://arxiv.org/abs/2006.13135v4
|
https://arxiv.org/pdf/2006.13135v4.pdf
|
https://github.com/ai-med/causal-effects-in-alzheimers-continuum
| true | true | true |
none
|
https://paperswithcode.com/paper/structured-ensembles-an-approach-to-reduce
|
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
|
2105.02551
|
https://arxiv.org/abs/2105.02551v2
|
https://arxiv.org/pdf/2105.02551v2.pdf
|
https://github.com/jaryP/StructuredEnsemble
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/benchmarking-graph-neural-networks
|
Benchmarking Graph Neural Networks
|
2003.00982
|
https://arxiv.org/abs/2003.00982v5
|
https://arxiv.org/pdf/2003.00982v5.pdf
|
https://github.com/PaddlePaddle/PGL/tree/master/examples/GaAN
| false | false | true |
paddle
|
https://paperswithcode.com/paper/a-feasibility-study-of-a-hyperparameter
|
A hyperparameter-tuning approach to automated inverse planning
|
2105.07024
|
https://arxiv.org/abs/2105.07024v2
|
https://arxiv.org/pdf/2105.07024v2.pdf
|
https://github.com/kels271828/RayBay
| true | false | true |
none
|
https://paperswithcode.com/paper/xai-n-sensor-based-robot-navigation-using
|
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees
|
2104.10818
|
https://arxiv.org/abs/2104.10818v2
|
https://arxiv.org/pdf/2104.10818v2.pdf
|
https://github.com/AMR-/JackalCrowdEnv
| true | true | true |
none
|
https://paperswithcode.com/paper/soft-actor-critic-for-discrete-action
|
Soft Actor-Critic for Discrete Action Settings
|
1910.07207
|
https://arxiv.org/abs/1910.07207v2
|
https://arxiv.org/pdf/1910.07207v2.pdf
|
https://github.com/Bigpig4396/PyTorch-Soft-Actor-Critic-SAC
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/sample-condensation-in-online-continual
|
Sample Condensation in Online Continual Learning
|
2206.11849
|
https://arxiv.org/abs/2206.11849v1
|
https://arxiv.org/pdf/2206.11849v1.pdf
|
https://github.com/MattiaSangermano/OLCGM
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/a-categorical-archive-of-chatgpt-failures
|
A Categorical Archive of ChatGPT Failures
|
2302.03494
|
https://arxiv.org/abs/2302.03494v8
|
https://arxiv.org/pdf/2302.03494v8.pdf
|
https://github.com/aliborji/chatgpt_failures
| true | true | true |
none
|
https://paperswithcode.com/paper/the-impact-of-technologies-in-political
|
The Impact of Technologies in Political Campaigns
|
1909.07644
|
http://arxiv.org/abs/1909.07644v1
|
http://arxiv.org/pdf/1909.07644v1.pdf
|
https://github.com/moritzhoferer/moritzhoferer
| false | false | true |
none
|
https://paperswithcode.com/paper/binary-search-trees-for-generalized
|
Binary search trees for generalized measurement
|
0712.2665
|
https://arxiv.org/abs/0712.2665v1
|
https://arxiv.org/pdf/0712.2665v1.pdf
|
https://github.com/petr-ivashkov/dynamic-circuit-povms
| false | false | true |
none
|
https://paperswithcode.com/paper/leveraging-local-distributions-in-mendelian
|
Leveraging Local Distributions in Mendelian Randomization: Uncertain Opinions are Invalid
|
2402.02329
|
https://arxiv.org/abs/2402.02329v1
|
https://arxiv.org/pdf/2402.02329v1.pdf
|
https://github.com/saili0103/mr-local
| true | true | false |
none
|
https://paperswithcode.com/paper/one-model-packs-thousands-of-items-with
|
One model Packs Thousands of Items with Recurrent Conditional Query Learning
|
2111.06726
|
https://arxiv.org/abs/2111.06726v1
|
https://arxiv.org/pdf/2111.06726v1.pdf
|
https://github.com/dongdongbh/RCQL
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/able-nerf-attention-based-rendering-with
|
ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field
|
2303.13817
|
https://arxiv.org/abs/2303.13817v1
|
https://arxiv.org/pdf/2303.13817v1.pdf
|
https://github.com/tangzj/able-nerf
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/spontaneous-conformal-symmetry-breaking-and-a
|
Spontaneous conformal symmetry breaking and a massless Wu-Yang monopole
|
1707.05325
|
http://arxiv.org/abs/1707.05325v1
|
http://arxiv.org/pdf/1707.05325v1.pdf
|
https://github.com/gillioz/SUSY-algebra
| false | false | true |
none
|
https://paperswithcode.com/paper/warm-starting-cma-es-for-hyperparameter
|
Warm Starting CMA-ES for Hyperparameter Optimization
|
2012.06932
|
https://arxiv.org/abs/2012.06932v1
|
https://arxiv.org/pdf/2012.06932v1.pdf
|
https://github.com/c-bata/benchmark-warm-starting-cmaes
| false | false | true |
none
|
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