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$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression | 110 | neurips | 21 | 1 | 2023-06-16 16:07:33.417000 | https://github.com/jacobi93/alpha-iou | 154 | -IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression | https://scholar.google.com/scholar?cluster=6960142602186458983&hl=en&as_sdt=0,5 | 5 | 2,021 |
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient | 24 | neurips | 18 | 8 | 2023-06-16 16:07:33.618000 | https://github.com/google-research/FirstOrderLp.jl | 77 | Practical large-scale linear programming using primal-dual hybrid gradient | https://scholar.google.com/scholar?cluster=15174638035980967431&hl=en&as_sdt=0,43 | 13 | 2,021 |
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions | 8 | neurips | 3 | 0 | 2023-06-16 16:07:33.819000 | https://github.com/ignavierng/local-astar | 10 | Reliable causal discovery with improved exact search and weaker assumptions | https://scholar.google.com/scholar?cluster=15393722733482596224&hl=en&as_sdt=0,5 | 3 | 2,021 |
Node Dependent Local Smoothing for Scalable Graph Learning | 19 | neurips | 1 | 2 | 2023-06-16 16:07:34.019000 | https://github.com/zwt233/ndls | 15 | Node dependent local smoothing for scalable graph learning | https://scholar.google.com/scholar?cluster=6608453490006216987&hl=en&as_sdt=0,33 | 2 | 2,021 |
Across-animal odor decoding by probabilistic manifold alignment | 1 | neurips | 1 | 0 | 2023-06-16 16:07:34.219000 | https://github.com/pedroherrerovidal/amlds | 4 | Across-animal odor decoding by probabilistic manifold alignment | https://scholar.google.com/scholar?cluster=14107653280115649019&hl=en&as_sdt=0,5 | 1 | 2,021 |
Excess Capacity and Backdoor Poisoning | 14 | neurips | 0 | 0 | 2023-06-16 16:07:34.419000 | https://github.com/narenmanoj/mnist-adv-training | 2 | Excess capacity and backdoor poisoning | https://scholar.google.com/scholar?cluster=13952393692022590215&hl=en&as_sdt=0,5 | 1 | 2,021 |
BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market | 4 | neurips | 2 | 1 | 2023-06-16 16:07:34.618000 | https://github.com/ZSCDumin/BCORLE | 5 | BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market | https://scholar.google.com/scholar?cluster=9674170088897673060&hl=en&as_sdt=0,21 | 2 | 2,021 |
Generic Neural Architecture Search via Regression | 13 | neurips | 9 | 2 | 2023-06-16 16:07:34.818000 | https://github.com/leeyeehoo/GenNAS | 35 | Generic neural architecture search via regression | https://scholar.google.com/scholar?cluster=17264205069746943313&hl=en&as_sdt=0,5 | 3 | 2,021 |
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration | 27 | neurips | 4 | 2 | 2023-06-16 16:07:35.018000 | https://github.com/sparisi/cbet | 30 | Interesting object, curious agent: Learning task-agnostic exploration | https://scholar.google.com/scholar?cluster=17517132874362052805&hl=en&as_sdt=0,47 | 1 | 2,021 |
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement | 1 | neurips | 0 | 0 | 2023-06-16 16:07:35.218000 | https://github.com/heyangqin/simigrad | 1 | SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement | https://scholar.google.com/scholar?cluster=13956766250705409738&hl=en&as_sdt=0,33 | 0 | 2,021 |
Implicit Regularization in Matrix Sensing via Mirror Descent | 5 | neurips | 0 | 0 | 2023-06-16 16:07:35.419000 | https://github.com/fawuuu/irmsmd | 0 | Implicit regularization in matrix sensing via mirror descent | https://scholar.google.com/scholar?cluster=1552182046702461253&hl=en&as_sdt=0,3 | 1 | 2,021 |
Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs | 8 | neurips | 5 | 7 | 2023-06-16 16:07:35.618000 | https://github.com/yujia-yan/skipping-the-frame-level | 47 | Skipping the frame-level: Event-based piano transcription with neural semi-crfs | https://scholar.google.com/scholar?cluster=5485151064368059296&hl=en&as_sdt=0,47 | 6 | 2,021 |
Deep Learning on a Data Diet: Finding Important Examples Early in Training | 98 | neurips | 18 | 1 | 2023-06-16 16:07:35.817000 | https://github.com/mansheej/data_diet | 73 | Deep learning on a data diet: Finding important examples early in training | https://scholar.google.com/scholar?cluster=6692350500928309521&hl=en&as_sdt=0,29 | 4 | 2,021 |
Auditing Black-Box Prediction Models for Data Minimization Compliance | 7 | neurips | 0 | 0 | 2023-06-16 16:07:36.017000 | https://github.com/rastegarpanah/data-minimization-auditor | 3 | Auditing black-box prediction models for data minimization compliance | https://scholar.google.com/scholar?cluster=14874021960575881635&hl=en&as_sdt=0,5 | 2 | 2,021 |
Meta Internal Learning | 5 | neurips | 2 | 0 | 2023-06-16 16:07:36.218000 | https://github.com/RaphaelBensTAU/MetaInternalLearning | 11 | Meta internal learning | https://scholar.google.com/scholar?cluster=16305601992312989829&hl=en&as_sdt=0,43 | 2 | 2,021 |
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis | 30 | neurips | 5 | 1 | 2023-06-16 16:07:36.418000 | https://github.com/sheldontsui/gof_neurips2021 | 100 | Generative occupancy fields for 3d surface-aware image synthesis | https://scholar.google.com/scholar?cluster=17796152118908275759&hl=en&as_sdt=0,47 | 14 | 2,021 |
Local policy search with Bayesian optimization | 8 | neurips | 6 | 0 | 2023-06-16 16:07:36.630000 | https://github.com/sarmueller/gibo | 6 | Local policy search with Bayesian optimization | https://scholar.google.com/scholar?cluster=12884901871071371472&hl=en&as_sdt=0,14 | 2 | 2,021 |
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks | 25 | neurips | 3 | 0 | 2023-06-16 16:07:36.830000 | https://github.com/nm-sparsity/dominosearch | 12 | DominoSearch: Find layer-wise fine-grained N: M sparse schemes from dense neural networks | https://scholar.google.com/scholar?cluster=12253443518394083686&hl=en&as_sdt=0,1 | 1 | 2,021 |
Techniques for Symbol Grounding with SATNet | 9 | neurips | 2 | 0 | 2023-06-16 16:07:37.030000 | https://github.com/SeverTopan/SATNet | 7 | Techniques for symbol grounding with SATNet | https://scholar.google.com/scholar?cluster=10654873214439307966&hl=en&as_sdt=0,33 | 2 | 2,021 |
Object DGCNN: 3D Object Detection using Dynamic Graphs | 46 | neurips | 116 | 47 | 2023-06-16 16:07:37.230000 | https://github.com/wangyueft/detr3d | 607 | Object dgcnn: 3d object detection using dynamic graphs | https://scholar.google.com/scholar?cluster=4400840303049250796&hl=en&as_sdt=0,38 | 20 | 2,021 |
Safe Policy Optimization with Local Generalized Linear Function Approximations | 2 | neurips | 2 | 0 | 2023-06-16 16:07:37.431000 | https://github.com/akifumi-wachi-4/spolf | 6 | Safe Policy Optimization with Local Generalized Linear Function Approximations | https://scholar.google.com/scholar?cluster=5085292587764280618&hl=en&as_sdt=0,11 | 2 | 2,021 |
The balancing principle for parameter choice in distance-regularized domain adaptation | 2 | neurips | 1 | 1 | 2023-06-16 16:07:37.632000 | https://github.com/xpitfire/bpda | 5 | The balancing principle for parameter choice in distance-regularized domain adaptation | https://scholar.google.com/scholar?cluster=7370752937301100335&hl=en&as_sdt=0,33 | 4 | 2,021 |
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring | 10 | neurips | 5 | 3 | 2023-06-16 16:07:37.832000 | https://github.com/cszcwu/gkmnet | 21 | Gaussian kernel mixture network for single image defocus deblurring | https://scholar.google.com/scholar?cluster=12551867425600364926&hl=en&as_sdt=0,5 | 1 | 2,021 |
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge | 21 | neurips | 2 | 0 | 2023-06-16 16:07:38.033000 | https://github.com/boone891214/mest | 15 | Mest: Accurate and fast memory-economic sparse training framework on the edge | https://scholar.google.com/scholar?cluster=4772832212685237675&hl=en&as_sdt=0,44 | 1 | 2,021 |
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods | 93 | neurips | 16 | 4 | 2023-06-16 16:07:38.233000 | https://github.com/cuai/non-homophily-large-scale | 78 | Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods | https://scholar.google.com/scholar?cluster=580916846840497144&hl=en&as_sdt=0,33 | 5 | 2,021 |
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example | 17 | neurips | 27 | 3 | 2023-06-16 16:07:38.433000 | https://github.com/galgreshler/Catch-A-Waveform | 139 | Catch-a-waveform: Learning to generate audio from a single short example | https://scholar.google.com/scholar?cluster=16318229752393122559&hl=en&as_sdt=0,5 | 4 | 2,021 |
Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective | 24 | neurips | 9 | 1 | 2023-06-16 16:07:38.633000 | https://github.com/VITA-Group/Ultra-Data-Efficient-GAN-Training | 79 | Data-efficient gan training beyond (just) augmentations: A lottery ticket perspective | https://scholar.google.com/scholar?cluster=2933094985071684054&hl=en&as_sdt=0,32 | 14 | 2,021 |
When Are Solutions Connected in Deep Networks? | 1,057 | neurips | 0 | 0 | 2023-06-16 16:07:38.834000 | https://github.com/modeconnectivity/modeconnectivity | 1 | Shortcut learning in deep neural networks | https://scholar.google.com/scholar?cluster=8900616021122454496&hl=en&as_sdt=0,5 | 1 | 2,021 |
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation | 12 | neurips | 4 | 1 | 2023-06-16 16:07:39.033000 | https://github.com/haoang97/tohan | 9 | TOHAN: A one-step approach towards few-shot hypothesis adaptation | https://scholar.google.com/scholar?cluster=3362363617253826009&hl=en&as_sdt=0,34 | 1 | 2,021 |
Learning Graph Cellular Automata | 16 | neurips | 10 | 0 | 2023-06-16 16:07:39.234000 | https://github.com/danielegrattarola/gnca | 40 | Learning graph cellular automata | https://scholar.google.com/scholar?cluster=4711762577281942253&hl=en&as_sdt=0,5 | 3 | 2,021 |
Efficient Online Estimation of Causal Effects by Deciding What to Observe | 8 | neurips | 0 | 0 | 2023-06-16 16:07:39.434000 | https://github.com/acmi-lab/online-moment-selection | 6 | Efficient online estimation of causal effects by deciding what to observe | https://scholar.google.com/scholar?cluster=200941432468169658&hl=en&as_sdt=0,5 | 2 | 2,021 |
Variational Multi-Task Learning with Gumbel-Softmax Priors | 11 | neurips | 1 | 0 | 2023-06-16 16:07:39.634000 | https://github.com/autumn9999/vmtl | 8 | Variational multi-task learning with Gumbel-softmax priors | https://scholar.google.com/scholar?cluster=979168555779336414&hl=en&as_sdt=0,11 | 2 | 2,021 |
Accelerating Quadratic Optimization with Reinforcement Learning | 16 | neurips | 15 | 0 | 2023-06-16 16:07:39.834000 | https://github.com/berkeleyautomation/rlqp | 75 | Accelerating quadratic optimization with reinforcement learning | https://scholar.google.com/scholar?cluster=3276389589139369906&hl=en&as_sdt=0,26 | 10 | 2,021 |
Deep Residual Learning in Spiking Neural Networks | 134 | neurips | 15 | 9 | 2023-06-16 16:07:40.034000 | https://github.com/fangwei123456/Spike-Element-Wise-ResNet | 78 | Deep residual learning in spiking neural networks | https://scholar.google.com/scholar?cluster=13799567303335562143&hl=en&as_sdt=0,5 | 3 | 2,021 |
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation | 10 | neurips | 4 | 1 | 2023-06-16 16:07:40.234000 | https://github.com/zhengzx-nlp/reder | 13 | Duplex sequence-to-sequence learning for reversible machine translation | https://scholar.google.com/scholar?cluster=7004295426093526403&hl=en&as_sdt=0,5 | 3 | 2,021 |
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks | 44 | neurips | 2 | 1 | 2023-06-16 16:07:40.434000 | https://github.com/papers-submission/structured_transposable_masks | 29 | Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks | https://scholar.google.com/scholar?cluster=17844164362787871979&hl=en&as_sdt=0,44 | 1 | 2,021 |
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning | 15 | neurips | 5 | 0 | 2023-06-16 16:07:40.635000 | https://github.com/hangxu0304/DeepReduce | 9 | Deepreduce: A sparse-tensor communication framework for federated deep learning | https://scholar.google.com/scholar?cluster=12891448574066341486&hl=en&as_sdt=0,47 | 1 | 2,021 |
Exploiting Domain-Specific Features to Enhance Domain Generalization | 44 | neurips | 2 | 1 | 2023-06-16 16:07:40.836000 | https://github.com/vinairesearch/mdsdi | 15 | Exploiting domain-specific features to enhance domain generalization | https://scholar.google.com/scholar?cluster=4543966632677300341&hl=en&as_sdt=0,5 | 0 | 2,021 |
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer | 6 | neurips | 3 | 0 | 2023-06-16 16:07:41.036000 | https://github.com/ZidiXiu/CRT | 11 | Supercharging imbalanced data learning with energy-based contrastive representation transfer | https://scholar.google.com/scholar?cluster=10778774199177050175&hl=en&as_sdt=0,5 | 1 | 2,021 |
Disrupting Deep Uncertainty Estimation Without Harming Accuracy | 4 | neurips | 1 | 0 | 2023-06-16 16:07:41.236000 | https://github.com/IdoGalil/ACE | 3 | Disrupting deep uncertainty estimation without harming accuracy | https://scholar.google.com/scholar?cluster=11133839384441962400&hl=en&as_sdt=0,33 | 2 | 2,021 |
Task-Adaptive Neural Network Search with Meta-Contrastive Learning | 4 | neurips | 6 | 0 | 2023-06-16 16:07:41.436000 | https://github.com/wyjeong/tans | 16 | Task-adaptive neural network search with meta-contrastive learning | https://scholar.google.com/scholar?cluster=11693856033014005643&hl=en&as_sdt=0,43 | 3 | 2,021 |
Neural Flows: Efficient Alternative to Neural ODEs | 22 | neurips | 13 | 2 | 2023-06-16 16:07:41.636000 | https://github.com/mbilos/neural-flows-experiments | 67 | Neural flows: Efficient alternative to neural ODEs | https://scholar.google.com/scholar?cluster=18217547123817497623&hl=en&as_sdt=0,39 | 3 | 2,021 |
End-to-end reconstruction meets data-driven regularization for inverse problems | 16 | neurips | 0 | 0 | 2023-06-16 16:07:41.836000 | https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization | 4 | End-to-end reconstruction meets data-driven regularization for inverse problems | https://scholar.google.com/scholar?cluster=16248522739800820583&hl=en&as_sdt=0,14 | 1 | 2,021 |
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs | 17 | neurips | 11 | 0 | 2023-06-16 16:07:42.037000 | https://github.com/thinklab-sjtu/ppo-bihyb | 73 | A bi-level framework for learning to solve combinatorial optimization on graphs | https://scholar.google.com/scholar?cluster=9298076485127002860&hl=en&as_sdt=0,10 | 3 | 2,021 |
When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? | 62 | neurips | 2 | 5 | 2023-06-16 16:07:42.237000 | https://github.com/lijiefan/advcl | 41 | When does contrastive learning preserve adversarial robustness from pretraining to finetuning? | https://scholar.google.com/scholar?cluster=3038595225265579627&hl=en&as_sdt=0,43 | 2 | 2,021 |
Learning to Predict Trustworthiness with Steep Slope Loss | 3 | neurips | 0 | 1 | 2023-06-16 16:07:42.437000 | https://github.com/luoyan407/predict_trustworthiness | 5 | Learning to predict trustworthiness with steep slope loss | https://scholar.google.com/scholar?cluster=8106650061212447650&hl=en&as_sdt=0,23 | 1 | 2,021 |
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay | 17 | neurips | 1 | 0 | 2023-06-16 16:07:42.637000 | https://github.com/tipt0p/periodic_behavior_bn_wd | 3 | On the periodic behavior of neural network training with batch normalization and weight decay | https://scholar.google.com/scholar?cluster=15045687956314005194&hl=en&as_sdt=0,5 | 2 | 2,021 |
NeRV: Neural Representations for Videos | 60 | neurips | 18 | 1 | 2023-06-16 16:07:42.837000 | https://github.com/haochen-rye/nerv | 234 | Nerv: Neural representations for videos | https://scholar.google.com/scholar?cluster=73059912539981135&hl=en&as_sdt=0,19 | 8 | 2,021 |
Generative vs. Discriminative: Rethinking The Meta-Continual Learning | 9 | neurips | 0 | 0 | 2023-06-16 16:07:43.037000 | https://github.com/aminbana/gemcl | 5 | Generative vs. discriminative: Rethinking the meta-continual learning | https://scholar.google.com/scholar?cluster=13601389422673314728&hl=en&as_sdt=0,5 | 2 | 2,021 |
Rethinking Graph Transformers with Spectral Attention | 156 | neurips | 31 | 1 | 2023-06-16 16:07:43.238000 | https://github.com/DevinKreuzer/SAN | 113 | Rethinking graph transformers with spectral attention | https://scholar.google.com/scholar?cluster=15947585912676378001&hl=en&as_sdt=0,10 | 6 | 2,021 |
Perceptual Score: What Data Modalities Does Your Model Perceive? | 12 | neurips | 1 | 0 | 2023-06-16 16:07:43.437000 | https://github.com/itaigat/perceptual-score | 8 | Perceptual score: What data modalities does your model perceive? | https://scholar.google.com/scholar?cluster=15852788555752209518&hl=en&as_sdt=0,5 | 1 | 2,021 |
PiRank: Scalable Learning To Rank via Differentiable Sorting | 8 | neurips | 8 | 3 | 2023-06-16 16:07:43.637000 | https://github.com/ermongroup/pirank | 58 | Pirank: Scalable learning to rank via differentiable sorting | https://scholar.google.com/scholar?cluster=8617942621344232575&hl=en&as_sdt=0,3 | 8 | 2,021 |
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data | 51 | neurips | 23 | 4 | 2023-06-16 16:07:43.837000 | https://github.com/endlesssora/deceived | 246 | Deceive D: adaptive pseudo augmentation for GAN training with limited data | https://scholar.google.com/scholar?cluster=4433178012946526426&hl=en&as_sdt=0,29 | 17 | 2,021 |
Variational Diffusion Models | 289 | neurips | 16 | 7 | 2023-06-16 16:07:44.037000 | https://github.com/google-research/vdm | 195 | Variational diffusion models | https://scholar.google.com/scholar?cluster=6024265554705485514&hl=en&as_sdt=0,33 | 4 | 2,021 |
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition | 17 | neurips | 133 | 24 | 2023-06-16 16:07:44.237000 | https://github.com/microsoft/NeuralSpeech | 1,007 | Fastcorrect: Fast error correction with edit alignment for automatic speech recognition | https://scholar.google.com/scholar?cluster=5241252993966056956&hl=en&as_sdt=0,11 | 30 | 2,021 |
Hierarchical Reinforcement Learning with Timed Subgoals | 12 | neurips | 2 | 0 | 2023-06-16 16:07:44.436000 | https://github.com/martius-lab/hits | 24 | Hierarchical reinforcement learning with timed subgoals | https://scholar.google.com/scholar?cluster=15547085409137841678&hl=en&as_sdt=0,5 | 3 | 2,021 |
SNIPS: Solving Noisy Inverse Problems Stochastically | 64 | neurips | 4 | 0 | 2023-06-16 16:07:44.636000 | https://github.com/bahjat-kawar/snips_torch | 38 | SNIPS: Solving noisy inverse problems stochastically | https://scholar.google.com/scholar?cluster=4461341669386556106&hl=en&as_sdt=0,5 | 1 | 2,021 |
Stateful ODE-Nets using Basis Function Expansions | 9 | neurips | 6 | 1 | 2023-06-16 16:07:44.837000 | https://github.com/afqueiruga/StatefulOdeNets | 38 | Stateful ode-nets using basis function expansions | https://scholar.google.com/scholar?cluster=5210524906297832917&hl=en&as_sdt=0,47 | 7 | 2,021 |
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? | 73 | neurips | 3 | 4 | 2023-06-16 16:07:45.037000 | https://github.com/vita-epfl/ttt-plus-plus | 49 | TTT++: When does self-supervised test-time training fail or thrive? | https://scholar.google.com/scholar?cluster=3286823258483076490&hl=en&as_sdt=0,11 | 5 | 2,021 |
Boosted CVaR Classification | 8 | neurips | 0 | 0 | 2023-06-16 16:07:45.238000 | https://github.com/runtianz/boosted_cvar | 4 | Boosted cvar classification | https://scholar.google.com/scholar?cluster=15164821511040155182&hl=en&as_sdt=0,5 | 2 | 2,021 |
SOLQ: Segmenting Objects by Learning Queries | 65 | neurips | 20 | 4 | 2023-06-16 16:07:45.437000 | https://github.com/megvii-research/SOLQ | 180 | Solq: Segmenting objects by learning queries | https://scholar.google.com/scholar?cluster=1852377411269249881&hl=en&as_sdt=0,5 | 10 | 2,021 |
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models | 24 | neurips | 4 | 0 | 2023-06-16 16:07:45.638000 | https://github.com/Physics-aware-AI/DiffCoSim | 21 | Extending lagrangian and hamiltonian neural networks with differentiable contact models | https://scholar.google.com/scholar?cluster=1516550074609182504&hl=en&as_sdt=0,15 | 1 | 2,021 |
Few-Shot Segmentation via Cycle-Consistent Transformer | 55 | neurips | 1 | 0 | 2023-06-16 16:07:45.838000 | https://github.com/GengDavid/CyCTR | 4 | Few-shot segmentation via cycle-consistent transformer | https://scholar.google.com/scholar?cluster=12634091315159410445&hl=en&as_sdt=0,39 | 2 | 2,021 |
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks | 58 | neurips | 3 | 0 | 2023-06-16 16:07:46.038000 | https://github.com/karolismart/dropgnn | 21 | DropGNN: Random dropouts increase the expressiveness of graph neural networks | https://scholar.google.com/scholar?cluster=6783529052723520360&hl=en&as_sdt=0,39 | 1 | 2,021 |
Searching Parameterized AP Loss for Object Detection | 1 | neurips | 3 | 0 | 2023-06-16 16:07:46.238000 | https://github.com/fundamentalvision/parameterized-ap-loss | 46 | Searching parameterized AP loss for object detection | https://scholar.google.com/scholar?cluster=99102542694531912&hl=en&as_sdt=0,33 | 2 | 2,021 |
NeuroMLR: Robust & Reliable Route Recommendation on Road Networks | 4 | neurips | 4 | 1 | 2023-06-16 16:07:46.440000 | https://github.com/idea-iitd/neuromlr | 9 | NeuroMLR: Robust & Reliable Route Recommendation on Road Networks | https://scholar.google.com/scholar?cluster=10547772011796748524&hl=en&as_sdt=0,5 | 1 | 2,021 |
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning | 65 | neurips | 8 | 6 | 2023-06-16 16:07:46.639000 | https://github.com/hzhupku/semiseg-ael | 112 | Semi-supervised semantic segmentation via adaptive equalization learning | https://scholar.google.com/scholar?cluster=11624791894491431600&hl=en&as_sdt=0,5 | 5 | 2,021 |
Comprehensive Knowledge Distillation with Causal Intervention | 20 | neurips | 2 | 0 | 2023-06-16 16:07:46.839000 | https://github.com/xiang-deng-dl/cid | 12 | Comprehensive knowledge distillation with causal intervention | https://scholar.google.com/scholar?cluster=2381368202143761298&hl=en&as_sdt=0,5 | 1 | 2,021 |
Two steps to risk sensitivity | 5 | neurips | 1 | 0 | 2023-06-16 16:07:47.040000 | https://github.com/crgagne/twosteps_neurips2021 | 2 | Two steps to risk sensitivity | https://scholar.google.com/scholar?cluster=11403909575499559814&hl=en&as_sdt=0,10 | 2 | 2,021 |
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization | 4 | neurips | 1 | 0 | 2023-06-16 16:07:47.241000 | https://github.com/ondrejbohdal/evograd | 18 | Evograd: Efficient gradient-based meta-learning and hyperparameter optimization | https://scholar.google.com/scholar?cluster=6358521501110876720&hl=en&as_sdt=0,33 | 2 | 2,021 |
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration | 2 | neurips | 0 | 0 | 2023-06-16 16:07:47.441000 | https://github.com/sylydya/sparse-deep-learning-a-new-framework-immuneto-local-traps-and-miscalibration | 0 | Sparse deep learning: A new framework immune to local traps and miscalibration | https://scholar.google.com/scholar?cluster=9056246695961108406&hl=en&as_sdt=0,11 | 1 | 2,021 |
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References | 20 | neurips | 10 | 2 | 2023-06-16 16:07:47.642000 | https://github.com/facebookresearch/Noresqa | 49 | NORESQA: A framework for speech quality assessment using non-matching references | https://scholar.google.com/scholar?cluster=7363609071396561507&hl=en&as_sdt=0,5 | 6 | 2,021 |
AFEC: Active Forgetting of Negative Transfer in Continual Learning | 19 | neurips | 1 | 1 | 2023-06-16 16:07:47.842000 | https://github.com/lywang3081/AFEC | 15 | AFEC: Active forgetting of negative transfer in continual learning | https://scholar.google.com/scholar?cluster=16155786595918509496&hl=en&as_sdt=0,11 | 1 | 2,021 |
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization | 6 | neurips | 3 | 0 | 2023-06-16 16:07:48.042000 | https://github.com/shengroup/mpmab_beacon | 0 | Heterogeneous multi-player multi-armed bandits: Closing the gap and generalization | https://scholar.google.com/scholar?cluster=4342595432442676512&hl=en&as_sdt=0,5 | 1 | 2,021 |
SWAD: Domain Generalization by Seeking Flat Minima | 142 | neurips | 16 | 0 | 2023-06-16 16:07:48.243000 | https://github.com/khanrc/swad | 124 | Swad: Domain generalization by seeking flat minima | https://scholar.google.com/scholar?cluster=17399407021631973298&hl=en&as_sdt=0,5 | 2 | 2,021 |
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting | 339 | neurips | 288 | 0 | 2023-06-16 16:07:48.443000 | https://github.com/thuml/autoformer | 1,148 | Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting | https://scholar.google.com/scholar?cluster=3122351390757400654&hl=en&as_sdt=0,22 | 13 | 2,021 |
Predicting Event Memorability from Contextual Visual Semantics | 1 | neurips | 0 | 0 | 2023-06-16 16:07:48.644000 | https://github.com/ffzzy840304/predicting-event-memorability | 0 | Predicting Event Memorability from Contextual Visual Semantics | https://scholar.google.com/scholar?cluster=12697030383321085621&hl=en&as_sdt=0,5 | 1 | 2,021 |
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning | 29 | neurips | 53 | 4 | 2023-06-16 16:07:48.844000 | https://github.com/zixuanke/pycontinual | 211 | Achieving forgetting prevention and knowledge transfer in continual learning | https://scholar.google.com/scholar?cluster=8575145504672099483&hl=en&as_sdt=0,5 | 5 | 2,021 |
Combiner: Full Attention Transformer with Sparse Computation Cost | 38 | neurips | 7,321 | 1,026 | 2023-06-16 16:07:49.043000 | https://github.com/google-research/google-research | 29,786 | Combiner: Full attention transformer with sparse computation cost | https://scholar.google.com/scholar?cluster=397201754720393524&hl=en&as_sdt=0,5 | 727 | 2,021 |
Geometry Processing with Neural Fields | 35 | neurips | 18 | 0 | 2023-06-16 16:07:49.244000 | https://github.com/stevenygd/nfgp | 175 | Geometry processing with neural fields | https://scholar.google.com/scholar?cluster=9959525918645208605&hl=en&as_sdt=0,5 | 9 | 2,021 |
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network | 21 | neurips | 50 | 0 | 2023-06-16 16:07:49.444000 | https://github.com/JusperLee/AFRCNN-For-Speech-Separation | 120 | Speech separation using an asynchronous fully recurrent convolutional neural network | https://scholar.google.com/scholar?cluster=11722770519480068778&hl=en&as_sdt=0,5 | 5 | 2,021 |
NAS-Bench-x11 and the Power of Learning Curves | 15 | neurips | 4 | 2 | 2023-06-16 16:07:49.644000 | https://github.com/automl/nas-bench-x11 | 17 | Nas-bench-x11 and the power of learning curves | https://scholar.google.com/scholar?cluster=13249979735452010353&hl=en&as_sdt=0,20 | 13 | 2,021 |
Learning Disentangled Behavior Embeddings | 8 | neurips | 0 | 0 | 2023-06-16 16:07:49.845000 | https://github.com/mishne-lab/dbe-disentangled-behavior-embedding | 13 | Learning disentangled behavior embeddings | https://scholar.google.com/scholar?cluster=15061877753853905670&hl=en&as_sdt=0,5 | 2 | 2,021 |
Sparse Flows: Pruning Continuous-depth Models | 12 | neurips | 22 | 9 | 2023-06-16 16:07:50.046000 | https://github.com/lucaslie/torchprune | 146 | Sparse flows: Pruning continuous-depth models | https://scholar.google.com/scholar?cluster=14652867200651009298&hl=en&as_sdt=0,5 | 5 | 2,021 |
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks | 59 | neurips | 14 | 0 | 2023-06-16 16:07:50.246000 | https://github.com/BorealisAI/SLAPS-GNN | 66 | SLAPS: Self-supervision improves structure learning for graph neural networks | https://scholar.google.com/scholar?cluster=13514640295473095313&hl=en&as_sdt=0,47 | 5 | 2,021 |
Aligning Pretraining for Detection via Object-Level Contrastive Learning | 74 | neurips | 19 | 16 | 2023-06-16 16:07:50.445000 | https://github.com/hologerry/SoCo | 156 | Aligning pretraining for detection via object-level contrastive learning | https://scholar.google.com/scholar?cluster=9757750069113028831&hl=en&as_sdt=0,44 | 7 | 2,021 |
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization | 7 | neurips | 0 | 0 | 2023-06-16 16:07:50.645000 | https://github.com/travers-rhodes/jlonevae | 1 | Local Disentanglement in Variational Auto-Encoders Using Jacobian Regularization | https://scholar.google.com/scholar?cluster=6881834482710680851&hl=en&as_sdt=0,5 | 1 | 2,021 |
Encoding Spatial Distribution of Convolutional Features for Texture Representation | 11 | neurips | 2 | 4 | 2023-06-16 16:07:50.847000 | https://github.com/csfengli/fenet | 10 | Encoding spatial distribution of convolutional features for texture representation | https://scholar.google.com/scholar?cluster=17445922379003065477&hl=en&as_sdt=0,43 | 1 | 2,021 |
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds | 34 | neurips | 3 | 1 | 2023-06-16 16:07:51.047000 | https://github.com/yjhuangcd/local-lipschitz | 18 | Training certifiably robust neural networks with efficient local lipschitz bounds | https://scholar.google.com/scholar?cluster=17265131367455074862&hl=en&as_sdt=0,43 | 3 | 2,021 |
Counterexample Guided RL Policy Refinement Using Bayesian Optimization | 4 | neurips | 0 | 1 | 2023-06-16 16:07:51.252000 | https://github.com/britig/policy-refinement-bo | 1 | Counterexample guided RL policy refinement using bayesian optimization | https://scholar.google.com/scholar?cluster=477353423111121794&hl=en&as_sdt=0,25 | 1 | 2,021 |
A Variational Perspective on Diffusion-Based Generative Models and Score Matching | 91 | neurips | 13 | 0 | 2023-06-16 16:07:51.454000 | https://github.com/CW-Huang/sdeflow-light | 99 | A variational perspective on diffusion-based generative models and score matching | https://scholar.google.com/scholar?cluster=11086576557599019726&hl=en&as_sdt=0,5 | 3 | 2,021 |
Causal Influence Detection for Improving Efficiency in Reinforcement Learning | 27 | neurips | 1 | 0 | 2023-06-16 16:07:51.656000 | https://github.com/martius-lab/cid-in-rl | 26 | Causal influence detection for improving efficiency in reinforcement learning | https://scholar.google.com/scholar?cluster=9354463069793604013&hl=en&as_sdt=0,5 | 4 | 2,021 |
Cycle Self-Training for Domain Adaptation | 70 | neurips | 4 | 3 | 2023-06-16 16:07:51.859000 | https://github.com/Liuhong99/CST | 39 | Cycle self-training for domain adaptation | https://scholar.google.com/scholar?cluster=18057534663552819958&hl=en&as_sdt=0,31 | 3 | 2,021 |
Optimal Policies Tend To Seek Power | 21 | neurips | 1 | 0 | 2023-06-16 16:07:52.059000 | https://github.com/loganriggs/optimal-policies-tend-to-seek-power | 0 | Optimal policies tend to seek power | https://scholar.google.com/scholar?cluster=2244318566147213779&hl=en&as_sdt=0,29 | 2 | 2,021 |
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair | 18 | neurips | 14 | 8 | 2023-06-16 16:07:52.261000 | https://github.com/google-research/plur | 86 | PLUR: A unifying, graph-based view of program learning, understanding, and repair | https://scholar.google.com/scholar?cluster=17073370459198177510&hl=en&as_sdt=0,14 | 11 | 2,021 |
COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining | 122 | neurips | 13 | 2 | 2023-06-16 16:07:52.462000 | https://github.com/microsoft/coco-lm | 112 | Coco-lm: Correcting and contrasting text sequences for language model pretraining | https://scholar.google.com/scholar?cluster=4355255601645727108&hl=en&as_sdt=0,14 | 4 | 2,021 |
XDO: A Double Oracle Algorithm for Extensive-Form Games | 29 | neurips | 8 | 0 | 2023-06-16 16:07:52.672000 | https://github.com/indylab/nxdo | 27 | XDO: A double oracle algorithm for extensive-form games | https://scholar.google.com/scholar?cluster=14117190087630680195&hl=en&as_sdt=0,5 | 4 | 2,021 |
Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations | 2 | neurips | 1 | 0 | 2023-06-16 16:07:52.880000 | https://github.com/vihari/aaa | 2 | Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations | https://scholar.google.com/scholar?cluster=10657209211783824075&hl=en&as_sdt=0,44 | 2 | 2,021 |
Probabilistic Margins for Instance Reweighting in Adversarial Training | 19 | neurips | 1 | 0 | 2023-06-16 16:07:53.081000 | https://github.com/qizhouwang/mail | 10 | Probabilistic margins for instance reweighting in adversarial training | https://scholar.google.com/scholar?cluster=6438754136382937945&hl=en&as_sdt=0,10 | 1 | 2,021 |
The Difficulty of Passive Learning in Deep Reinforcement Learning | 25 | neurips | 2,436 | 170 | 2023-06-16 16:07:53.282000 | https://github.com/deepmind/deepmind-research | 11,904 | The difficulty of passive learning in deep reinforcement learning | https://scholar.google.com/scholar?cluster=4514798007776798220&hl=en&as_sdt=0,6 | 336 | 2,021 |
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