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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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