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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence | 1,977 | neurips | 162 | 17 | 2023-06-16 15:09:54.035000 | https://github.com/google-research/fixmatch | 990 | Fixmatch: Simplifying semi-supervised learning with consistency and confidence | https://scholar.google.com/scholar?cluster=8436393078669287497&hl=en&as_sdt=0,34 | 19 | 2,020 |
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing | 66 | neurips | 7 | 1 | 2023-06-16 15:09:54.226000 | https://github.com/google-research/tf-opt | 31 | Reinforcement learning with combinatorial actions: An application to vehicle routing | https://scholar.google.com/scholar?cluster=10633025590595233619&hl=en&as_sdt=0,43 | 10 | 2,020 |
Causal Intervention for Weakly-Supervised Semantic Segmentation | 242 | neurips | 28 | 12 | 2023-06-16 15:09:54.442000 | https://github.com/ZHANGDONG-NJUST/CONTA | 178 | Causal intervention for weakly-supervised semantic segmentation | https://scholar.google.com/scholar?cluster=6645460811692278989&hl=en&as_sdt=0,33 | 5 | 2,020 |
Debugging Tests for Model Explanations | 126 | neurips | 2 | 0 | 2023-06-16 15:09:54.635000 | https://github.com/adebayoj/explaindebug | 3 | Debugging tests for model explanations | https://scholar.google.com/scholar?cluster=15051438141959870127&hl=en&as_sdt=0,5 | 3 | 2,020 |
Robust compressed sensing using generative models | 25 | neurips | 1 | 0 | 2023-06-16 15:09:54.826000 | https://github.com/ajiljalal/csgm-robust-neurips | 8 | Robust compressed sensing using generative models | https://scholar.google.com/scholar?cluster=11462485595148288562&hl=en&as_sdt=0,5 | 2 | 2,020 |
Adapting Neural Architectures Between Domains | 23 | neurips | 1 | 1 | 2023-06-16 15:09:55.017000 | https://github.com/liyxi/AdaptNAS | 7 | Adapting neural architectures between domains | https://scholar.google.com/scholar?cluster=15474765041948411848&hl=en&as_sdt=0,10 | 1 | 2,020 |
Learning Guidance Rewards with Trajectory-space Smoothing | 22 | neurips | 1 | 1 | 2023-06-16 15:09:55.211000 | https://github.com/tgangwani/GuidanceRewards | 10 | Learning guidance rewards with trajectory-space smoothing | https://scholar.google.com/scholar?cluster=16129997703943948282&hl=en&as_sdt=0,33 | 3 | 2,020 |
Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding | 29 | neurips | 0 | 0 | 2023-06-16 15:09:55.404000 | https://github.com/rsonthal/TreeRep | 21 | Tree! i am no tree! i am a low dimensional hyperbolic embedding | https://scholar.google.com/scholar?cluster=18232158800489906399&hl=en&as_sdt=0,5 | 3 | 2,020 |
Deep Structural Causal Models for Tractable Counterfactual Inference | 118 | neurips | 49 | 7 | 2023-06-16 15:09:55.596000 | https://github.com/biomedia-mira/deepscm | 224 | Deep structural causal models for tractable counterfactual inference | https://scholar.google.com/scholar?cluster=9027210436245269282&hl=en&as_sdt=0,18 | 9 | 2,020 |
Convolutional Generation of Textured 3D Meshes | 39 | neurips | 17 | 5 | 2023-06-16 15:09:55.790000 | https://github.com/dariopavllo/convmesh | 107 | Convolutional generation of textured 3d meshes | https://scholar.google.com/scholar?cluster=10601781187163028035&hl=en&as_sdt=0,5 | 5 | 2,020 |
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks | 18 | neurips | 1 | 0 | 2023-06-16 15:09:55.982000 | https://github.com/cjf00000/StatQuant | 21 | A statistical framework for low-bitwidth training of deep neural networks | https://scholar.google.com/scholar?cluster=11151412933346353614&hl=en&as_sdt=0,34 | 1 | 2,020 |
Better Set Representations For Relational Reasoning | 14 | neurips | 2 | 1 | 2023-06-16 15:09:56.175000 | https://github.com/CUVL/SSLR | 29 | Better set representations for relational reasoning | https://scholar.google.com/scholar?cluster=6489896145456654265&hl=en&as_sdt=0,5 | 6 | 2,020 |
Primal-Dual Mesh Convolutional Neural Networks | 75 | neurips | 18 | 10 | 2023-06-16 15:09:56.368000 | https://github.com/MIT-SPARK/PD-MeshNet | 97 | Primal-dual mesh convolutional neural networks | https://scholar.google.com/scholar?cluster=12375851352098825949&hl=en&as_sdt=0,5 | 7 | 2,020 |
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning | 33 | neurips | 0 | 0 | 2023-06-16 15:09:56.583000 | https://github.com/dGiulia/ConditionalMetaLearning | 2 | The advantage of conditional meta-learning for biased regularization and fine tuning | https://scholar.google.com/scholar?cluster=2418967028251018198&hl=en&as_sdt=0,10 | 2 | 2,020 |
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks | 47 | neurips | 5 | 1 | 2023-06-16 15:09:56.777000 | https://github.com/tsingqguo/ABBA | 27 | Watch out! motion is blurring the vision of your deep neural networks | https://scholar.google.com/scholar?cluster=15773966474412221565&hl=en&as_sdt=0,33 | 2 | 2,020 |
Bayesian Deep Ensembles via the Neural Tangent Kernel | 74 | neurips | 6 | 0 | 2023-06-16 15:09:56.970000 | https://github.com/bobby-he/bayesian-ntk | 21 | Bayesian deep ensembles via the neural tangent kernel | https://scholar.google.com/scholar?cluster=10890964373773286236&hl=en&as_sdt=0,5 | 3 | 2,020 |
Adaptive Sampling for Stochastic Risk-Averse Learning | 52 | neurips | 1 | 0 | 2023-06-16 15:09:57.163000 | https://github.com/sebascuri/adacvar | 6 | Adaptive sampling for stochastic risk-averse learning | https://scholar.google.com/scholar?cluster=10094126690067053033&hl=en&as_sdt=0,5 | 2 | 2,020 |
Taming Discrete Integration via the Boon of Dimensionality | 6 | neurips | 1 | 1 | 2023-06-16 15:09:57.356000 | https://github.com/meelgroup/deweight | 0 | Taming discrete integration via the boon of dimensionality | https://scholar.google.com/scholar?cluster=17208976171354395854&hl=en&as_sdt=0,36 | 3 | 2,020 |
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond | 150 | neurips | 47 | 15 | 2023-06-16 15:09:57.549000 | https://github.com/KaidiXu/auto_LiRPA | 208 | Automatic perturbation analysis for scalable certified robustness and beyond | https://scholar.google.com/scholar?cluster=346708359742349242&hl=en&as_sdt=0,33 | 8 | 2,020 |
Conservative Q-Learning for Offline Reinforcement Learning | 854 | neurips | 61 | 16 | 2023-06-16 15:09:57.742000 | https://github.com/aviralkumar2907/CQL | 305 | Conservative q-learning for offline reinforcement learning | https://scholar.google.com/scholar?cluster=7056274634823343559&hl=en&as_sdt=0,5 | 6 | 2,020 |
Ensembling geophysical models with Bayesian Neural Networks | 18 | neurips | 2 | 0 | 2023-06-16 15:09:57.935000 | https://github.com/Ushnish-Sengupta/Model-Ensembler | 9 | Ensembling geophysical models with Bayesian neural networks | https://scholar.google.com/scholar?cluster=12898556235367158665&hl=en&as_sdt=0,5 | 2 | 2,020 |
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation | 24 | neurips | 24 | 10 | 2023-06-16 15:09:58.132000 | https://github.com/lyxok1/STM-Training | 106 | Delving into the cyclic mechanism in semi-supervised video object segmentation | https://scholar.google.com/scholar?cluster=15310731299697520994&hl=en&as_sdt=0,25 | 6 | 2,020 |
Understanding Deep Architecture with Reasoning Layer | 12 | neurips | 0 | 0 | 2023-06-16 15:09:58.324000 | https://github.com/xinshi-chen/Deep-Architecture-With-Reasoning-Layer | 5 | Understanding deep architecture with reasoning layer | https://scholar.google.com/scholar?cluster=8179923820884933954&hl=en&as_sdt=0,33 | 1 | 2,020 |
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming | 3 | neurips | 0 | 0 | 2023-06-16 15:09:58.516000 | https://github.com/joehuchette/reserve-price-optimization | 2 | Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming | https://scholar.google.com/scholar?cluster=13519296265519190525&hl=en&as_sdt=0,47 | 1 | 2,020 |
Learning to search efficiently for causally near-optimal treatments | 7 | neurips | 1 | 0 | 2023-06-16 15:09:58.708000 | https://github.com/Healthy-AI/TreatmentExploration | 1 | Learning to search efficiently for causally near-optimal treatments | https://scholar.google.com/scholar?cluster=11107205422193494167&hl=en&as_sdt=0,5 | 1 | 2,020 |
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts | 80 | neurips | 8 | 0 | 2023-06-16 15:09:58.899000 | https://github.com/sharpenb/Posterior-Network | 59 | Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts | https://scholar.google.com/scholar?cluster=13793786839752857625&hl=en&as_sdt=0,5 | 2 | 2,020 |
A causal view of compositional zero-shot recognition | 76 | neurips | 2 | 2 | 2023-06-16 15:09:59.091000 | https://github.com/nv-research-israel/causal_comp | 27 | A causal view of compositional zero-shot recognition | https://scholar.google.com/scholar?cluster=2543173389101020482&hl=en&as_sdt=0,10 | 6 | 2,020 |
HiPPO: Recurrent Memory with Optimal Polynomial Projections | 76 | neurips | 18 | 1 | 2023-06-16 15:09:59.284000 | https://github.com/HazyResearch/hippo-code | 92 | Hippo: Recurrent memory with optimal polynomial projections | https://scholar.google.com/scholar?cluster=10897171960502189367&hl=en&as_sdt=0,33 | 20 | 2,020 |
Auto Learning Attention | 24 | neurips | 3 | 1 | 2023-06-16 15:09:59.476000 | https://github.com/btma48/AutoLA | 21 | Auto learning attention | https://scholar.google.com/scholar?cluster=4640609275657710063&hl=en&as_sdt=0,36 | 4 | 2,020 |
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect | 291 | neurips | 67 | 25 | 2023-06-16 15:09:59.669000 | https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch | 531 | Long-tailed classification by keeping the good and removing the bad momentum causal effect | https://scholar.google.com/scholar?cluster=11307578533103322862&hl=en&as_sdt=0,44 | 12 | 2,020 |
Deep Archimedean Copulas | 12 | neurips | 2 | 1 | 2023-06-16 15:09:59.862000 | https://github.com/lingchunkai/ACNet | 8 | Deep archimedean copulas | https://scholar.google.com/scholar?cluster=453186630159063437&hl=en&as_sdt=0,5 | 1 | 2,020 |
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization | 67 | neurips | 5 | 2 | 2023-06-16 15:10:00.056000 | https://github.com/facebookresearch/alebo | 35 | Re-examining linear embeddings for high-dimensional Bayesian optimization | https://scholar.google.com/scholar?cluster=7963529277112461610&hl=en&as_sdt=0,5 | 10 | 2,020 |
Neural Networks Fail to Learn Periodic Functions and How to Fix It | 46 | neurips | 1 | 0 | 2023-06-16 15:10:00.250000 | https://github.com/AdenosHermes/NeurIPS_2020_Snake | 25 | Neural networks fail to learn periodic functions and how to fix it | https://scholar.google.com/scholar?cluster=16056803791186814907&hl=en&as_sdt=0,5 | 5 | 2,020 |
Distribution Matching for Crowd Counting | 166 | neurips | 50 | 14 | 2023-06-16 15:10:00.593000 | https://github.com/cvlab-stonybrook/DM-Count | 185 | Distribution matching for crowd counting | https://scholar.google.com/scholar?cluster=14310555288407205229&hl=en&as_sdt=0,5 | 8 | 2,020 |
Correspondence learning via linearly-invariant embedding | 40 | neurips | 3 | 1 | 2023-06-16 15:10:00.786000 | https://github.com/riccardomarin/Diff-FMaps | 10 | Correspondence learning via linearly-invariant embedding | https://scholar.google.com/scholar?cluster=6342234500198528456&hl=en&as_sdt=0,20 | 2 | 2,020 |
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning | 146 | neurips | 70 | 3 | 2023-06-16 15:10:00.982000 | https://github.com/zcajiayin/L2D | 193 | Learning to dispatch for job shop scheduling via deep reinforcement learning | https://scholar.google.com/scholar?cluster=17946575832024706335&hl=en&as_sdt=0,44 | 1 | 2,020 |
On Adaptive Attacks to Adversarial Example Defenses | 615 | neurips | 12 | 2 | 2023-06-16 15:10:01.176000 | https://github.com/wielandbrendel/adaptive_attacks_paper | 78 | On adaptive attacks to adversarial example defenses | https://scholar.google.com/scholar?cluster=5574467727525147588&hl=en&as_sdt=0,35 | 8 | 2,020 |
Ultrahyperbolic Representation Learning | 11 | neurips | 0 | 0 | 2023-06-16 15:10:01.368000 | https://github.com/MarcTLaw/UltrahyperbolicRepresentation | 11 | Ultrahyperbolic representation learning | https://scholar.google.com/scholar?cluster=15522026458695881889&hl=en&as_sdt=0,5 | 6 | 2,020 |
Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms | 17 | neurips | 2 | 0 | 2023-06-16 15:10:01.561000 | https://github.com/khashayarkhv/many-armed-bandit | 4 | Unreasonable effectiveness of greedy algorithms in multi-armed bandit with many arms | https://scholar.google.com/scholar?cluster=1593756484114132419&hl=en&as_sdt=0,5 | 1 | 2,020 |
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction | 20 | neurips | 5 | 0 | 2023-06-16 15:10:01.754000 | https://github.com/JannerM/gamma-models | 38 | gamma-models: Generative temporal difference learning for infinite-horizon prediction | https://scholar.google.com/scholar?cluster=16924243578285273048&hl=en&as_sdt=0,4 | 8 | 2,020 |
Efficient Exact Verification of Binarized Neural Networks | 40 | neurips | 3 | 0 | 2023-06-16 15:10:01.946000 | https://github.com/jia-kai/eevbnn | 10 | Efficient exact verification of binarized neural networks | https://scholar.google.com/scholar?cluster=3950117023454899474&hl=en&as_sdt=0,16 | 3 | 2,020 |
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback | 35 | neurips | 4 | 2 | 2023-06-16 15:10:02.138000 | https://github.com/RyanWangZf/CVIB-Rec | 23 | Information theoretic counterfactual learning from missing-not-at-random feedback | https://scholar.google.com/scholar?cluster=2026070403857564388&hl=en&as_sdt=0,47 | 3 | 2,020 |
Language Models are Few-Shot Learners | 11,121 | neurips | 2,202 | 3 | 2023-06-16 15:10:02.330000 | https://github.com/openai/gpt-3 | 15,171 | Language models are few-shot learners | https://scholar.google.com/scholar?cluster=15953747982133883426&hl=en&as_sdt=0,41 | 881 | 2,020 |
MomentumRNN: Integrating Momentum into Recurrent Neural Networks | 24 | neurips | 6 | 2 | 2023-06-16 15:10:02.522000 | https://github.com/minhtannguyen/MomentumRNN | 16 | Momentumrnn: Integrating momentum into recurrent neural networks | https://scholar.google.com/scholar?cluster=9149151218987275930&hl=en&as_sdt=0,10 | 1 | 2,020 |
Projected Stein Variational Gradient Descent | 42 | neurips | 1 | 1 | 2023-06-16 15:10:02.714000 | https://github.com/cpempire/pSVGD | 7 | Projected Stein variational gradient descent | https://scholar.google.com/scholar?cluster=11787408032214941846&hl=en&as_sdt=0,5 | 2 | 2,020 |
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks | 24 | neurips | 1 | 14 | 2023-06-16 15:10:02.907000 | https://github.com/z-fabian/transfer_lowerbounds_arXiv | 2 | Minimax lower bounds for transfer learning with linear and one-hidden layer neural networks | https://scholar.google.com/scholar?cluster=8519029442558083621&hl=en&as_sdt=0,3 | 2 | 2,020 |
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks | 354 | neurips | 61 | 11 | 2023-06-16 15:10:03.098000 | https://github.com/FabianFuchsML/se3-transformer-public | 388 | Se (3)-transformers: 3d roto-translation equivariant attention networks | https://scholar.google.com/scholar?cluster=7114881113669802193&hl=en&as_sdt=0,9 | 15 | 2,020 |
On the equivalence of molecular graph convolution and molecular wave function with poor basis set | 10 | neurips | 39 | 0 | 2023-06-16 15:10:03.292000 | https://github.com/masashitsubaki/QuantumDeepField_molecule | 164 | On the equivalence of molecular graph convolution and molecular wave function with poor basis set | https://scholar.google.com/scholar?cluster=15706248090993034000&hl=en&as_sdt=0,5 | 3 | 2,020 |
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model | 20 | neurips | 11 | 7 | 2023-06-16 15:10:03.484000 | https://github.com/SteffenCzolbe/PerceptualSimilarity | 90 | A loss function for generative neural networks based on watson's perceptual model | https://scholar.google.com/scholar?cluster=2642015369120549708&hl=en&as_sdt=0,33 | 4 | 2,020 |
Adversarial Robustness of Supervised Sparse Coding | 16 | neurips | 4 | 0 | 2023-06-16 15:10:03.681000 | https://github.com/Sulam-Group/Adversarial-Robust-Supervised-Sparse-Coding | 2 | Adversarial robustness of supervised sparse coding | https://scholar.google.com/scholar?cluster=7092140439598020620&hl=en&as_sdt=0,34 | 3 | 2,020 |
Network Diffusions via Neural Mean-Field Dynamics | 5 | neurips | 1 | 0 | 2023-06-16 15:10:03.874000 | https://github.com/ShushanHe/neural-mf | 4 | Network diffusions via neural mean-field dynamics | https://scholar.google.com/scholar?cluster=17188160558828611858&hl=en&as_sdt=0,33 | 1 | 2,020 |
Rethinking pooling in graph neural networks | 78 | neurips | 11 | 3 | 2023-06-16 15:10:04.066000 | https://github.com/AaltoPML/Rethinking-pooling-in-GNNs | 53 | Rethinking pooling in graph neural networks | https://scholar.google.com/scholar?cluster=7929818342253962258&hl=en&as_sdt=0,39 | 7 | 2,020 |
Rescuing neural spike train models from bad MLE | 3 | neurips | 3 | 0 | 2023-06-16 15:10:04.261000 | https://github.com/diegoarri91/mmd-glm | 6 | Rescuing neural spike train models from bad MLE | https://scholar.google.com/scholar?cluster=3646921033072503899&hl=en&as_sdt=0,33 | 3 | 2,020 |
Deep Imitation Learning for Bimanual Robotic Manipulation | 24 | neurips | 4 | 2 | 2023-06-16 15:10:04.453000 | https://github.com/Rose-STL-Lab/HDR-IL | 27 | Deep imitation learning for bimanual robotic manipulation | https://scholar.google.com/scholar?cluster=3337481646096729028&hl=en&as_sdt=0,5 | 4 | 2,020 |
Stationary Activations for Uncertainty Calibration in Deep Learning | 15 | neurips | 5 | 0 | 2023-06-16 15:10:04.645000 | https://github.com/AaltoML/stationary-activations | 9 | Stationary activations for uncertainty calibration in deep learning | https://scholar.google.com/scholar?cluster=13291548217087481879&hl=en&as_sdt=0,33 | 3 | 2,020 |
On Power Laws in Deep Ensembles | 32 | neurips | 3 | 0 | 2023-06-16 15:10:04.837000 | https://github.com/nadiinchi/power_laws_deep_ensembles | 2 | On power laws in deep ensembles | https://scholar.google.com/scholar?cluster=14597524051325855513&hl=en&as_sdt=0,18 | 2 | 2,020 |
Practical Quasi-Newton Methods for Training Deep Neural Networks | 63 | neurips | 8 | 0 | 2023-06-16 15:10:05.029000 | https://github.com/renyiryry/kbfgs_neurips2020_public | 17 | Practical quasi-newton methods for training deep neural networks | https://scholar.google.com/scholar?cluster=16186200424986740304&hl=en&as_sdt=0,5 | 1 | 2,020 |
Consistent feature selection for analytic deep neural networks | 13 | neurips | 2 | 0 | 2023-06-16 15:10:05.222000 | https://github.com/vucdinh/alg-net | 2 | Consistent feature selection for analytic deep neural networks | https://scholar.google.com/scholar?cluster=4872208848076144978&hl=en&as_sdt=0,33 | 3 | 2,020 |
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification | 89 | neurips | 31 | 2 | 2023-06-16 15:10:05.414000 | https://github.com/blackfeather-wang/GFNet-Pytorch | 177 | Glance and focus: a dynamic approach to reducing spatial redundancy in image classification | https://scholar.google.com/scholar?cluster=229727098340388548&hl=en&as_sdt=0,33 | 5 | 2,020 |
Information Maximization for Few-Shot Learning | 125 | neurips | 18 | 3 | 2023-06-16 15:10:05.606000 | https://github.com/mboudiaf/TIM | 110 | Information maximization for few-shot learning | https://scholar.google.com/scholar?cluster=11018359707721193758&hl=en&as_sdt=0,6 | 6 | 2,020 |
Bayesian Robust Optimization for Imitation Learning | 26 | neurips | 0 | 0 | 2023-06-16 15:10:05.798000 | https://github.com/dsbrown1331/broil | 3 | Bayesian robust optimization for imitation learning | https://scholar.google.com/scholar?cluster=974540193771601354&hl=en&as_sdt=0,31 | 3 | 2,020 |
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance | 418 | neurips | 83 | 9 | 2023-06-16 15:10:05.990000 | https://github.com/lioryariv/idr | 586 | Multiview neural surface reconstruction by disentangling geometry and appearance | https://scholar.google.com/scholar?cluster=6952139627795921381&hl=en&as_sdt=0,5 | 16 | 2,020 |
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation | 24 | neurips | 3 | 7 | 2023-06-16 15:10:06.182000 | https://github.com/isapome/BrainProp | 14 | Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation | https://scholar.google.com/scholar?cluster=8542738772122027975&hl=en&as_sdt=0,47 | 2 | 2,020 |
Structured Prediction for Conditional Meta-Learning | 11 | neurips | 1 | 0 | 2023-06-16 15:10:06.374000 | https://github.com/RuohanW/Tasml | 6 | Structured prediction for conditional meta-learning | https://scholar.google.com/scholar?cluster=6688833579162281826&hl=en&as_sdt=0,38 | 3 | 2,020 |
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient | 67 | neurips | 1 | 0 | 2023-06-16 15:10:06.567000 | https://github.com/acnagle/optimal-lottery-tickets | 3 | Optimal lottery tickets via subset sum: Logarithmic over-parameterization is sufficient | https://scholar.google.com/scholar?cluster=8996425038613953094&hl=en&as_sdt=0,44 | 1 | 2,020 |
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes | 283 | neurips | 927 | 137 | 2023-06-16 15:10:06.760000 | https://github.com/facebookresearch/mmf | 5,242 | The hateful memes challenge: Detecting hate speech in multimodal memes | https://scholar.google.com/scholar?cluster=17728666238988121395&hl=en&as_sdt=0,33 | 117 | 2,020 |
Identifying Learning Rules From Neural Network Observables | 16 | neurips | 2 | 0 | 2023-06-16 15:10:06.952000 | https://github.com/neuroailab/lr-identify | 12 | Identifying learning rules from neural network observables | https://scholar.google.com/scholar?cluster=12719991320138828348&hl=en&as_sdt=0,33 | 6 | 2,020 |
Improving Policy-Constrained Kidney Exchange via Pre-Screening | 3 | neurips | 0 | 0 | 2023-06-16 15:10:07.144000 | https://github.com/duncanmcelfresh/kpd-edge-query | 0 | Improving policy-constrained kidney exchange via pre-screening | https://scholar.google.com/scholar?cluster=11750267690088441504&hl=en&as_sdt=0,22 | 3 | 2,020 |
Dual Instrumental Variable Regression | 63 | neurips | 1 | 0 | 2023-06-16 15:10:07.336000 | https://github.com/krikamol/DualIV-NeurIPS2020 | 1 | Dual instrumental variable regression | https://scholar.google.com/scholar?cluster=7206130195065971102&hl=en&as_sdt=0,25 | 4 | 2,020 |
Interventional Few-Shot Learning | 159 | neurips | 22 | 11 | 2023-06-16 15:10:07.528000 | https://github.com/yue-zhongqi/ifsl | 152 | Interventional few-shot learning | https://scholar.google.com/scholar?cluster=6986077950904335953&hl=en&as_sdt=0,23 | 7 | 2,020 |
ShiftAddNet: A Hardware-Inspired Deep Network | 53 | neurips | 16 | 5 | 2023-06-16 15:10:07.720000 | https://github.com/RICE-EIC/ShiftAddNet | 60 | Shiftaddnet: A hardware-inspired deep network | https://scholar.google.com/scholar?cluster=11143869055965605135&hl=en&as_sdt=0,33 | 3 | 2,020 |
Network-to-Network Translation with Conditional Invertible Neural Networks | 34 | neurips | 19 | 6 | 2023-06-16 15:10:07.912000 | https://github.com/CompVis/net2net | 212 | Network-to-network translation with conditional invertible neural networks | https://scholar.google.com/scholar?cluster=10385399504485528967&hl=en&as_sdt=0,5 | 13 | 2,020 |
Model-based Policy Optimization with Unsupervised Model Adaptation | 21 | neurips | 0 | 1 | 2023-06-16 15:10:08.103000 | https://github.com/RockySJ/ampo | 13 | Model-based policy optimization with unsupervised model adaptation | https://scholar.google.com/scholar?cluster=6711842689847231868&hl=en&as_sdt=0,15 | 4 | 2,020 |
Geometric All-way Boolean Tensor Decomposition | 3 | neurips | 0 | 0 | 2023-06-16 15:10:08.295000 | https://github.com/clwan/GETF | 1 | Geometric all-way boolean tensor decomposition | https://scholar.google.com/scholar?cluster=8557467909142317065&hl=en&as_sdt=0,25 | 1 | 2,020 |
Hold me tight! Influence of discriminative features on deep network boundaries | 41 | neurips | 1 | 0 | 2023-06-16 15:10:08.488000 | https://github.com/LTS4/hold-me-tight | 21 | Hold me tight! Influence of discriminative features on deep network boundaries | https://scholar.google.com/scholar?cluster=7593820950200684211&hl=en&as_sdt=0,7 | 5 | 2,020 |
Adversarial Self-Supervised Contrastive Learning | 169 | neurips | 17 | 5 | 2023-06-16 15:10:08.680000 | https://github.com/Kim-Minseon/RoCL | 161 | Adversarial self-supervised contrastive learning | https://scholar.google.com/scholar?cluster=13558288573789113152&hl=en&as_sdt=0,1 | 10 | 2,020 |
Learning to summarize with human feedback | 326 | neurips | 127 | 6 | 2023-06-16 15:10:08.872000 | https://github.com/openai/summarize-from-feedback | 785 | Learning to summarize with human feedback | https://scholar.google.com/scholar?cluster=14483287577780422045&hl=en&as_sdt=0,5 | 127 | 2,020 |
Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks | 3 | neurips | 1 | 0 | 2023-06-16 15:10:09.064000 | https://github.com/FrostHan/HetFFN- | 0 | Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks | https://scholar.google.com/scholar?cluster=818462062864224649&hl=en&as_sdt=0,44 | 2 | 2,020 |
Learning Dynamic Belief Graphs to Generalize on Text-Based Games | 84 | neurips | 11 | 1 | 2023-06-16 15:10:09.259000 | https://github.com/xingdi-eric-yuan/GATA-public | 33 | Learning dynamic belief graphs to generalize on text-based games | https://scholar.google.com/scholar?cluster=15134168610189625143&hl=en&as_sdt=0,10 | 4 | 2,020 |
Triple descent and the two kinds of overfitting: where & why do they appear? | 66 | neurips | 3 | 0 | 2023-06-16 15:10:09.452000 | https://github.com/sdascoli/triple-descent-paper | 7 | Triple descent and the two kinds of overfitting: Where & why do they appear? | https://scholar.google.com/scholar?cluster=16515586708066009664&hl=en&as_sdt=0,33 | 4 | 2,020 |
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization | 81 | neurips | 9 | 5 | 2023-06-16 15:10:09.644000 | https://github.com/wyf0912/LDDG | 54 | Domain generalization for medical imaging classification with linear-dependency regularization | https://scholar.google.com/scholar?cluster=7705964353868024891&hl=en&as_sdt=0,50 | 2 | 2,020 |
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? | 16 | neurips | 2 | 0 | 2023-06-16 15:10:09.836000 | https://github.com/GuoqiangWoodrowWu/MLC-theory | 5 | Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? | https://scholar.google.com/scholar?cluster=986985610325694720&hl=en&as_sdt=0,14 | 1 | 2,020 |
Adaptive Gradient Quantization for Data-Parallel SGD | 47 | neurips | 5 | 0 | 2023-06-16 15:10:10.028000 | https://github.com/tabrizian/learning-to-quantize | 20 | Adaptive gradient quantization for data-parallel sgd | https://scholar.google.com/scholar?cluster=1571526277141139654&hl=en&as_sdt=0,5 | 5 | 2,020 |
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies | 50 | neurips | 5 | 1 | 2023-06-16 15:10:10.220000 | https://github.com/itaigat/removing-bias-in-multi-modal-classifiers | 25 | Removing bias in multi-modal classifiers: Regularization by maximizing functional entropies | https://scholar.google.com/scholar?cluster=11041773532262485134&hl=en&as_sdt=0,5 | 1 | 2,020 |
Audeo: Audio Generation for a Silent Performance Video | 30 | neurips | 2 | 0 | 2023-06-16 15:10:10.412000 | https://github.com/shlizee/Audeo | 20 | Audeo: Audio generation for a silent performance video | https://scholar.google.com/scholar?cluster=13879342907781591680&hl=en&as_sdt=0,34 | 1 | 2,020 |
Community detection using fast low-cardinality semidefinite programming
| 3 | neurips | 1 | 0 | 2023-06-16 15:10:10.604000 | https://github.com/locuslab/sdp_clustering | 12 | Community detection using fast low-cardinality semidefinite programming | https://scholar.google.com/scholar?cluster=7593232396131727716&hl=en&as_sdt=0,33 | 5 | 2,020 |
Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement | 85 | neurips | 13 | 0 | 2023-06-16 15:10:10.796000 | https://github.com/xmlyqing00/AFB-URR | 83 | Video object segmentation with adaptive feature bank and uncertain-region refinement | https://scholar.google.com/scholar?cluster=13323746974008516937&hl=en&as_sdt=0,33 | 3 | 2,020 |
Inferring learning rules from animal decision-making | 18 | neurips | 0 | 0 | 2023-06-16 15:10:10.990000 | https://github.com/pillowlab/psytrack_learning | 8 | Inferring learning rules from animal decision-making | https://scholar.google.com/scholar?cluster=6158593368508995675&hl=en&as_sdt=0,5 | 8 | 2,020 |
Input-Aware Dynamic Backdoor Attack | 195 | neurips | 2 | 3 | 2023-06-16 15:10:11.183000 | https://github.com/VinAIResearch/input-aware-backdoor-attack-release | 9 | Input-aware dynamic backdoor attack | https://scholar.google.com/scholar?cluster=2116699235703044974&hl=en&as_sdt=0,33 | 2 | 2,020 |
Cross-Scale Internal Graph Neural Network for Image Super-Resolution | 109 | neurips | 36 | 9 | 2023-06-16 15:10:11.376000 | https://github.com/sczhou/IGNN | 289 | Cross-scale internal graph neural network for image super-resolution | https://scholar.google.com/scholar?cluster=10605222671754393608&hl=en&as_sdt=0,5 | 17 | 2,020 |
Restoring Negative Information in Few-Shot Object Detection | 46 | neurips | 8 | 5 | 2023-06-16 15:10:11.568000 | https://github.com/yang-yk/NP-RepMet | 27 | Restoring negative information in few-shot object detection | https://scholar.google.com/scholar?cluster=13837106915985694250&hl=en&as_sdt=0,5 | 3 | 2,020 |
Robust Correction of Sampling Bias using Cumulative Distribution Functions | 6 | neurips | 0 | 0 | 2023-06-16 15:10:11.760000 | https://github.com/honeybijan/NeurIPS2020 | 1 | Robust correction of sampling bias using cumulative distribution functions | https://scholar.google.com/scholar?cluster=14960787625732407840&hl=en&as_sdt=0,44 | 1 | 2,020 |
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation | 88 | neurips | 11 | 1 | 2023-06-16 15:10:11.952000 | https://github.com/kgl-prml/Pixel-Level-Cycle-Association | 86 | Pixel-level cycle association: A new perspective for domain adaptive semantic segmentation | https://scholar.google.com/scholar?cluster=15898877851209488916&hl=en&as_sdt=0,5 | 15 | 2,020 |
Classification with Valid and Adaptive Coverage | 108 | neurips | 6 | 0 | 2023-06-16 15:10:12.144000 | https://github.com/msesia/arc | 24 | Classification with valid and adaptive coverage | https://scholar.google.com/scholar?cluster=6435727128447832809&hl=en&as_sdt=0,37 | 2 | 2,020 |
Diverse Image Captioning with Context-Object Split Latent Spaces | 26 | neurips | 7 | 2 | 2023-06-16 15:10:12.335000 | https://github.com/visinf/cos-cvae | 35 | Diverse image captioning with context-object split latent spaces | https://scholar.google.com/scholar?cluster=8685721581290827769&hl=en&as_sdt=0,33 | 2 | 2,020 |
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts | 22 | neurips | 1 | 0 | 2023-06-16 15:10:12.529000 | https://github.com/mryab/learning-at-home | 42 | Towards crowdsourced training of large neural networks using decentralized mixture-of-experts | https://scholar.google.com/scholar?cluster=1517172184249734814&hl=en&as_sdt=0,44 | 5 | 2,020 |
Bidirectional Convolutional Poisson Gamma Dynamical Systems | 3 | neurips | 0 | 0 | 2023-06-16 15:10:12.721000 | https://github.com/BoChenGroup/BCPGDS | 0 | Bidirectional convolutional Poisson gamma dynamical systems | https://scholar.google.com/scholar?cluster=2617893906496182541&hl=en&as_sdt=0,33 | 1 | 2,020 |
Deep Reinforcement and InfoMax Learning | 77 | neurips | 4 | 0 | 2023-06-16 15:10:12.912000 | https://github.com/bmazoure/DRIML | 10 | Deep reinforcement and infomax learning | https://scholar.google.com/scholar?cluster=18204322956274436351&hl=en&as_sdt=0,39 | 3 | 2,020 |
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow | 11 | neurips | 0 | 1 | 2023-06-16 15:10:13.105000 | https://github.com/didriknielsen/pixelcnn_flow | 18 | Closing the dequantization gap: Pixelcnn as a single-layer flow | https://scholar.google.com/scholar?cluster=9793552037748729432&hl=en&as_sdt=0,5 | 6 | 2,020 |
All Word Embeddings from One Embedding | 12 | neurips | 3 | 0 | 2023-06-16 15:10:13.297000 | https://github.com/takase/alone_seq2seq | 26 | All word embeddings from one embedding | https://scholar.google.com/scholar?cluster=16025202978450671106&hl=en&as_sdt=0,21 | 5 | 2,020 |
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