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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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