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Fair Bayes-Optimal Classifiers Under Predictive Parity
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:40.538000
|
https://github.com/xianlizeng/fairbayes-dpp
| 1 |
Fair Bayes-Optimal Classifiers Under Predictive Parity
|
https://scholar.google.com/scholar?cluster=1276001185503240257&hl=en&as_sdt=0,5
| 1 | 2,022 |
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
| 8 |
neurips
| 11 | 1 |
2023-06-16 22:59:40.750000
|
https://github.com/hsouri/bayesiantransferlearning
| 98 |
Pre-train your loss: Easy bayesian transfer learning with informative priors
|
https://scholar.google.com/scholar?cluster=16170264225104963616&hl=en&as_sdt=0,34
| 5 | 2,022 |
Training language models to follow instructions with human feedback
| 1,152 |
neurips
| 123 | 3 |
2023-06-16 22:59:40.962000
|
https://github.com/openai/following-instructions-human-feedback
| 994 |
Training language models to follow instructions with human feedback
|
https://scholar.google.com/scholar?cluster=12979976309017799162&hl=en&as_sdt=0,10
| 114 | 2,022 |
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
| 6 |
neurips
| 9 | 3 |
2023-06-16 22:59:41.174000
|
https://github.com/rabbityl/deformationpyramid
| 97 |
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
|
https://scholar.google.com/scholar?cluster=6583649970645189814&hl=en&as_sdt=0,14
| 9 | 2,022 |
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:59:41.386000
|
https://github.com/olivierjeunen/disentangling-neurips-2022
| 2 |
Disentangling causal effects from sets of interventions in the presence of unobserved confounders
|
https://scholar.google.com/scholar?cluster=11308179641811912058&hl=en&as_sdt=0,11
| 2 | 2,022 |
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
| 3 |
neurips
| 2 | 0 |
2023-06-16 22:59:41.599000
|
https://github.com/hsg-aiml/neurips_2022-generative_hyper_representations
| 6 |
Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
|
https://scholar.google.com/scholar?cluster=5719530018346300751&hl=en&as_sdt=0,5
| 4 | 2,022 |
Flexible Diffusion Modeling of Long Videos
| 40 |
neurips
| 5 | 0 |
2023-06-16 22:59:41.812000
|
https://github.com/plai-group/flexible-video-diffusion-modeling
| 64 |
Flexible diffusion modeling of long videos
|
https://scholar.google.com/scholar?cluster=14027817982126481605&hl=en&as_sdt=0,5
| 5 | 2,022 |
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:59:42.023000
|
https://github.com/jw4hv/geo-sic
| 3 |
Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers
|
https://scholar.google.com/scholar?cluster=17159159244649320976&hl=en&as_sdt=0,47
| 1 | 2,022 |
Segmenting Moving Objects via an Object-Centric Layered Representation
| 9 |
neurips
| 0 | 0 |
2023-06-16 22:59:42.240000
|
https://github.com/Jyxarthur/OCLR_model
| 13 |
Segmenting moving objects via an object-centric layered representation
|
https://scholar.google.com/scholar?cluster=725725608410804919&hl=en&as_sdt=0,44
| 1 | 2,022 |
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
| 4 |
neurips
| 94 | 29 |
2023-06-16 22:59:42.461000
|
https://github.com/automl/NASLib
| 403 |
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies
|
https://scholar.google.com/scholar?cluster=12406911975162843820&hl=en&as_sdt=0,10
| 14 | 2,022 |
Controllable Text Generation with Neurally-Decomposed Oracle
| 3 |
neurips
| 2 | 0 |
2023-06-16 22:59:42.672000
|
https://github.com/mtsomethree/constrdecoding
| 11 |
Controllable Text Generation with Neurally-Decomposed Oracle
|
https://scholar.google.com/scholar?cluster=9870671818275677250&hl=en&as_sdt=0,31
| 4 | 2,022 |
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:42.883000
|
https://github.com/caselles/neurips22-demonstrations-pedagogy-pragmatism
| 0 |
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
|
https://scholar.google.com/scholar?cluster=13569490094707234684&hl=en&as_sdt=0,47
| 1 | 2,022 |
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
| 8 |
neurips
| 0 | 0 |
2023-06-16 22:59:43.096000
|
https://github.com/sbnietert/sliced-wp
| 0 |
Statistical, robustness, and computational guarantees for sliced wasserstein distances
|
https://scholar.google.com/scholar?cluster=13763656485291132199&hl=en&as_sdt=0,5
| 1 | 2,022 |
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
| 3 |
neurips
| 1 | 0 |
2023-06-16 22:59:43.308000
|
https://github.com/talshaharabany/what-is-where-by-looking
| 14 |
What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
|
https://scholar.google.com/scholar?cluster=887087732905998506&hl=en&as_sdt=0,11
| 1 | 2,022 |
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:59:43.521000
|
https://github.com/siqixu/deepmed
| 2 |
DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning
|
https://scholar.google.com/scholar?cluster=2967140859437535127&hl=en&as_sdt=0,5
| 1 | 2,022 |
A Continuous Time Framework for Discrete Denoising Models
| 10 |
neurips
| 4 | 1 |
2023-06-16 22:59:43.732000
|
https://github.com/andrew-cr/tauldr
| 20 |
A continuous time framework for discrete denoising models
|
https://scholar.google.com/scholar?cluster=12065158919379277046&hl=en&as_sdt=0,5
| 1 | 2,022 |
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:59:43.945000
|
https://github.com/qingguo666/FLO
| 9 |
Tight mutual information estimation with contrastive fenchel-legendre optimization
|
https://scholar.google.com/scholar?cluster=11580465232288190410&hl=en&as_sdt=0,5
| 2 | 2,022 |
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:59:44.157000
|
https://github.com/kid-7391/soprc
| 4 |
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
|
https://scholar.google.com/scholar?cluster=2443951446559121514&hl=en&as_sdt=0,5
| 1 | 2,022 |
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:59:44.369000
|
https://github.com/omerb01/transboost
| 6 |
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
|
https://scholar.google.com/scholar?cluster=7854158254206635581&hl=en&as_sdt=0,5
| 1 | 2,022 |
Sparse Probabilistic Circuits via Pruning and Growing
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:59:44.581000
|
https://github.com/ucla-starai/sparsepc
| 10 |
Sparse probabilistic circuits via pruning and growing
|
https://scholar.google.com/scholar?cluster=11141675136195823156&hl=en&as_sdt=0,3
| 2 | 2,022 |
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
| 10 |
neurips
| 3 | 0 |
2023-06-16 22:59:44.793000
|
https://github.com/feradauto/moralcot
| 29 |
When to make exceptions: Exploring language models as accounts of human moral judgment
|
https://scholar.google.com/scholar?cluster=15747656978235543700&hl=en&as_sdt=0,47
| 1 | 2,022 |
Exponential Family Model-Based Reinforcement Learning via Score Matching
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:45.005000
|
https://github.com/anmolkabra/score-matching-rl
| 2 |
Exponential family model-based reinforcement learning via score matching
|
https://scholar.google.com/scholar?cluster=13487904936270229304&hl=en&as_sdt=0,5
| 3 | 2,022 |
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
| 3 |
neurips
| 3 | 0 |
2023-06-16 22:59:45.216000
|
https://github.com/lamda-bbo/mcts-vs
| 14 |
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
|
https://scholar.google.com/scholar?cluster=11812344942865377060&hl=en&as_sdt=0,33
| 2 | 2,022 |
Assistive Teaching of Motor Control Tasks to Humans
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:45.429000
|
https://github.com/stanford-iliad/teaching
| 2 |
Assistive Teaching of Motor Control Tasks to Humans
|
https://scholar.google.com/scholar?cluster=4146414116857689554&hl=en&as_sdt=0,5
| 5 | 2,022 |
Adversarial Reprogramming Revisited
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:59:45.640000
|
https://github.com/englert-m/adversarial_reprogramming
| 2 |
Adversarial Reprogramming Revisited
|
https://scholar.google.com/scholar?cluster=5745042332144042845&hl=en&as_sdt=0,18
| 1 | 2,022 |
When Does Differentially Private Learning Not Suffer in High Dimensions?
| 11 |
neurips
| 17 | 4 |
2023-06-16 22:59:45.853000
|
https://github.com/lxuechen/private-transformers
| 100 |
When Does Differentially Private Learning Not Suffer in High Dimensions?
|
https://scholar.google.com/scholar?cluster=12738886860685825235&hl=en&as_sdt=0,5
| 5 | 2,022 |
Masked Autoencoders that Listen
| 45 |
neurips
| 24 | 11 |
2023-06-16 22:59:46.064000
|
https://github.com/facebookresearch/audiomae
| 344 |
Masked autoencoders that listen
|
https://scholar.google.com/scholar?cluster=13233494379811120690&hl=en&as_sdt=0,33
| 39 | 2,022 |
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
| 7 |
neurips
| 44 | 27 |
2023-06-16 22:59:46.276000
|
https://github.com/decile-team/cords
| 272 |
Automata: Gradient based data subset selection for compute-efficient hyper-parameter tuning
|
https://scholar.google.com/scholar?cluster=8803292945419400795&hl=en&as_sdt=0,5
| 10 | 2,022 |
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:46.489000
|
https://github.com/khalednakhleh/deeptop
| 0 |
DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs
|
https://scholar.google.com/scholar?cluster=13570932694368744960&hl=en&as_sdt=0,11
| 1 | 2,022 |
Is a Modular Architecture Enough?
| 11 |
neurips
| 3 | 0 |
2023-06-16 22:59:46.700000
|
https://github.com/sarthmit/mod_arch
| 31 |
Is a Modular Architecture Enough?
|
https://scholar.google.com/scholar?cluster=5707197899340562621&hl=en&as_sdt=0,5
| 2 | 2,022 |
Exploration via Planning for Information about the Optimal Trajectory
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:59:46.912000
|
https://github.com/fusion-ml/trajectory-information-rl
| 14 |
Exploration via planning for information about the optimal trajectory
|
https://scholar.google.com/scholar?cluster=14433349520441278180&hl=en&as_sdt=0,39
| 3 | 2,022 |
Subquadratic Kronecker Regression with Applications to Tensor Decomposition
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:59:47.124000
|
https://github.com/fahrbach/subquadratic-kronecker-regression
| 0 |
Subquadratic kronecker regression with applications to tensor decomposition
|
https://scholar.google.com/scholar?cluster=16694254702569927793&hl=en&as_sdt=0,33
| 2 | 2,022 |
Robust Anytime Learning of Markov Decision Processes
| 7 |
neurips
| 1 | 0 |
2023-06-16 22:59:47.336000
|
https://github.com/lava-lab/luiaard
| 2 |
Robust anytime learning of Markov decision processes
|
https://scholar.google.com/scholar?cluster=13918485196268093813&hl=en&as_sdt=0,39
| 2 | 2,022 |
Discovering Design Concepts for CAD Sketches
| 2 |
neurips
| 1 | 3 |
2023-06-16 22:59:47.561000
|
https://github.com/yyuezhi/sketchconcept
| 4 |
Discovering Design Concepts for CAD Sketches
|
https://scholar.google.com/scholar?cluster=13612243371244513176&hl=en&as_sdt=0,18
| 1 | 2,022 |
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:59:47.778000
|
https://github.com/zhiyuanyaoj/marllb
| 2 |
Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game
|
https://scholar.google.com/scholar?cluster=3976860628797287838&hl=en&as_sdt=0,14
| 2 | 2,022 |
NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:47.991000
|
https://github.com/pvnieo/ncp
| 2 |
NCP: Neural correspondence prior for effective unsupervised shape matching
|
https://scholar.google.com/scholar?cluster=3458823752936331324&hl=en&as_sdt=0,47
| 1 | 2,022 |
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models
| 5 |
neurips
| 7 | 1 |
2023-06-16 22:59:48.206000
|
https://github.com/lmxyy/sige
| 212 |
Efficient spatially sparse inference for conditional gans and diffusion models
|
https://scholar.google.com/scholar?cluster=949267028420813363&hl=en&as_sdt=0,5
| 5 | 2,022 |
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:59:48.420000
|
https://github.com/dritsa-konstantina/greparl
| 2 |
A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis
|
https://scholar.google.com/scholar?cluster=18337461361366657304&hl=en&as_sdt=0,5
| 2 | 2,022 |
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
| 11 |
neurips
| 11 | 2 |
2023-06-16 22:59:48.633000
|
https://github.com/google-research/reincarnating_rl
| 81 |
Reincarnating reinforcement learning: Reusing prior computation to accelerate progress
|
https://scholar.google.com/scholar?cluster=2191734016134843580&hl=en&as_sdt=0,25
| 7 | 2,022 |
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:48.858000
|
https://github.com/liangzu/irls-neurips2022
| 0 |
Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression
|
https://scholar.google.com/scholar?cluster=145441446786155398&hl=en&as_sdt=0,5
| 2 | 2,022 |
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:59:49.071000
|
https://github.com/manga-uofa/nacc
| 4 |
A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization
|
https://scholar.google.com/scholar?cluster=3534208302188234048&hl=en&as_sdt=0,5
| 1 | 2,022 |
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
| 8 |
neurips
| 0 | 0 |
2023-06-16 22:59:49.283000
|
https://github.com/ruikunzhou/unknown_neural_lyapunov
| 2 |
Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees
|
https://scholar.google.com/scholar?cluster=7399734234325202121&hl=en&as_sdt=0,33
| 1 | 2,022 |
A Lower Bound of Hash Codes' Performance
| 0 |
neurips
| 0 | 1 |
2023-06-16 22:59:49.499000
|
https://github.com/vl-group/lbhash
| 4 |
A Lower Bound of Hash Codes' Performance
|
https://scholar.google.com/scholar?cluster=1910707024863961077&hl=en&as_sdt=0,5
| 1 | 2,022 |
Self-Supervised Image Restoration with Blurry and Noisy Pairs
| 1 |
neurips
| 2 | 0 |
2023-06-16 22:59:49.722000
|
https://github.com/cszhilu1998/selfir
| 33 |
Self-Supervised Image Restoration with Blurry and Noisy Pairs
|
https://scholar.google.com/scholar?cluster=12118320256260943816&hl=en&as_sdt=0,10
| 1 | 2,022 |
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:49.943000
|
https://github.com/jy0205/stcat
| 25 |
Embracing consistency: A one-stage approach for spatio-temporal video grounding
|
https://scholar.google.com/scholar?cluster=2054637694993057366&hl=en&as_sdt=0,31
| 2 | 2,022 |
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
| 12 |
neurips
| 6 | 1 |
2023-06-16 22:59:50.159000
|
https://github.com/breakend/pileoflaw
| 63 |
Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset
|
https://scholar.google.com/scholar?cluster=16242802812264116024&hl=en&as_sdt=0,33
| 3 | 2,022 |
Patching open-vocabulary models by interpolating weights
| 29 |
neurips
| 5 | 0 |
2023-06-16 22:59:50.384000
|
https://github.com/mlfoundations/patching
| 66 |
Patching open-vocabulary models by interpolating weights
|
https://scholar.google.com/scholar?cluster=12287111402475287292&hl=en&as_sdt=0,10
| 6 | 2,022 |
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
| 3 |
neurips
| 3 | 0 |
2023-06-16 22:59:50.597000
|
https://github.com/craigleili/attentivefmaps
| 5 |
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
|
https://scholar.google.com/scholar?cluster=11801194413397973375&hl=en&as_sdt=0,5
| 4 | 2,022 |
Practical Adversarial Multivalid Conformal Prediction
| 13 |
neurips
| 5 | 0 |
2023-06-16 22:59:50.810000
|
https://github.com/progbelarus/multivalidprediction
| 12 |
Practical adversarial multivalid conformal prediction
|
https://scholar.google.com/scholar?cluster=6409760077625712140&hl=en&as_sdt=0,33
| 2 | 2,022 |
Test-Time Training with Masked Autoencoders
| 25 |
neurips
| 1 | 0 |
2023-06-16 22:59:51.021000
|
https://github.com/yossigandelsman/test_time_training_mae
| 48 |
Test-time training with masked autoencoders
|
https://scholar.google.com/scholar?cluster=2544097260576053446&hl=en&as_sdt=0,5
| 3 | 2,022 |
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
| 5 |
neurips
| 4 | 1 |
2023-06-16 22:59:51.246000
|
https://github.com/thorben-frank/mlff
| 30 |
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
|
https://scholar.google.com/scholar?cluster=16550039961851369955&hl=en&as_sdt=0,5
| 3 | 2,022 |
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
| 5 |
neurips
| 5 | 0 |
2023-06-16 22:59:51.480000
|
https://github.com/macderru/hyperdomainnet
| 76 |
Hyperdomainnet: Universal domain adaptation for generative adversarial networks
|
https://scholar.google.com/scholar?cluster=14001675056345163311&hl=en&as_sdt=0,10
| 3 | 2,022 |
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
| 13 |
neurips
| 3 | 1 |
2023-06-16 22:59:51.693000
|
https://github.com/glamor-usc/climb
| 39 |
Climb: A continual learning benchmark for vision-and-language tasks
|
https://scholar.google.com/scholar?cluster=2434194050994506336&hl=en&as_sdt=0,5
| 5 | 2,022 |
Bidirectional Learning for Offline Infinite-width Model-based Optimization
| 9 |
neurips
| 0 | 0 |
2023-06-16 22:59:51.904000
|
https://github.com/ggchen1997/bdi
| 7 |
Bidirectional learning for offline infinite-width model-based optimization
|
https://scholar.google.com/scholar?cluster=13019462606638546457&hl=en&as_sdt=0,29
| 2 | 2,022 |
Unified Optimal Transport Framework for Universal Domain Adaptation
| 3 |
neurips
| 3 | 0 |
2023-06-16 22:59:52.116000
|
https://github.com/changwxx/uniot-for-unida
| 29 |
Unified optimal transport framework for universal domain adaptation
|
https://scholar.google.com/scholar?cluster=16909534816090473474&hl=en&as_sdt=0,41
| 4 | 2,022 |
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:59:52.327000
|
https://github.com/haoyuzhao123/coreset-vfl-codes
| 3 |
Coresets for Vertical Federated Learning: Regularized Linear Regression and -Means Clustering
|
https://scholar.google.com/scholar?cluster=6637629427663807&hl=en&as_sdt=0,5
| 1 | 2,022 |
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:52.539000
|
https://github.com/movinghoon/mira
| 8 |
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
|
https://scholar.google.com/scholar?cluster=15425564159709405182&hl=en&as_sdt=0,32
| 1 | 2,022 |
Mind Reader: Reconstructing complex images from brain activities
| 10 |
neurips
| 3 | 3 |
2023-06-16 22:59:52.751000
|
https://github.com/sklin93/mind-reader
| 38 |
Mind Reader: Reconstructing complex images from brain activities
|
https://scholar.google.com/scholar?cluster=206404245897193541&hl=en&as_sdt=0,5
| 4 | 2,022 |
An Investigation into Whitening Loss for Self-supervised Learning
| 4 |
neurips
| 2 | 1 |
2023-06-16 22:59:52.963000
|
https://github.com/winci-ai/cw-rgp
| 12 |
An investigation into whitening loss for self-supervised learning
|
https://scholar.google.com/scholar?cluster=8085947162457980477&hl=en&as_sdt=0,10
| 1 | 2,022 |
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
| 3 |
neurips
| 2 | 0 |
2023-06-16 22:59:53.175000
|
https://github.com/chr26195/gkd
| 16 |
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
|
https://scholar.google.com/scholar?cluster=3988720192874696300&hl=en&as_sdt=0,33
| 2 | 2,022 |
A Benchmark for Compositional Visual Reasoning
| 4 |
neurips
| 2 | 0 |
2023-06-16 22:59:53.387000
|
https://github.com/aimzer/cvr
| 12 |
A benchmark for compositional visual reasoning
|
https://scholar.google.com/scholar?cluster=11272228500855667208&hl=en&as_sdt=0,14
| 1 | 2,022 |
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
| 0 |
neurips
| 3 | 0 |
2023-06-16 22:59:53.598000
|
https://github.com/nikihowe/myriad
| 45 |
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
|
https://scholar.google.com/scholar?cluster=6826074521392801836&hl=en&as_sdt=0,14
| 2 | 2,022 |
Batch Bayesian optimisation via density-ratio estimation with guarantees
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:53.810000
|
https://github.com/rafaol/batch-bore-with-guarantees
| 1 |
Batch Bayesian optimisation via density-ratio estimation with guarantees
|
https://scholar.google.com/scholar?cluster=17612558782197429855&hl=en&as_sdt=0,47
| 1 | 2,022 |
Amplifying Membership Exposure via Data Poisoning
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:59:54.022000
|
https://github.com/yfchen1994/poisoning_membership
| 9 |
Amplifying Membership Exposure via Data Poisoning
|
https://scholar.google.com/scholar?cluster=13772127157500094294&hl=en&as_sdt=0,31
| 1 | 2,022 |
BayesPCN: A Continually Learnable Predictive Coding Associative Memory
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:54.234000
|
https://github.com/plai-group/bayes-pcn
| 4 |
BayesPCN: A Continually Learnable Predictive Coding Associative Memory
|
https://scholar.google.com/scholar?cluster=6318188315590566524&hl=en&as_sdt=0,5
| 3 | 2,022 |
Semantic Probabilistic Layers for Neuro-Symbolic Learning
| 11 |
neurips
| 1 | 3 |
2023-06-16 22:59:54.460000
|
https://github.com/KareemYousrii/SPL
| 14 |
Semantic probabilistic layers for neuro-symbolic learning
|
https://scholar.google.com/scholar?cluster=790768995509318385&hl=en&as_sdt=0,33
| 5 | 2,022 |
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
| 10 |
neurips
| 6 | 2 |
2023-06-16 22:59:54.672000
|
https://github.com/haiyang-w/cagroup3d
| 67 |
Cagroup3d: Class-aware grouping for 3d object detection on point clouds
|
https://scholar.google.com/scholar?cluster=10922971019763222861&hl=en&as_sdt=0,5
| 7 | 2,022 |
Characterizing Datapoints via Second-Split Forgetting
| 5 |
neurips
| 0 | 0 |
2023-06-16 22:59:54.884000
|
https://github.com/pratyushmaini/ssft
| 8 |
Characterizing datapoints via second-split forgetting
|
https://scholar.google.com/scholar?cluster=15661926582422861854&hl=en&as_sdt=0,5
| 1 | 2,022 |
GENIE: Higher-Order Denoising Diffusion Solvers
| 17 |
neurips
| 2 | 0 |
2023-06-16 22:59:55.096000
|
https://github.com/nv-tlabs/GENIE
| 75 |
GENIE: Higher-order denoising diffusion solvers
|
https://scholar.google.com/scholar?cluster=7162863738522405281&hl=en&as_sdt=0,41
| 27 | 2,022 |
Tsetlin Machine for Solving Contextual Bandit Problems
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:59:55.308000
|
https://github.com/raihan-seraj/tsetlin-machine-for-solving-contextual-bandit-problems
| 0 |
Tsetlin Machine for Solving Contextual Bandit Problems
|
https://scholar.google.com/scholar?cluster=3151730412209496386&hl=en&as_sdt=0,3
| 2 | 2,022 |
Matryoshka Representation Learning
| 3 |
neurips
| 6 | 0 |
2023-06-16 22:59:55.520000
|
https://github.com/raivnlab/mrl
| 55 |
Matryoshka Representation Learning
|
https://scholar.google.com/scholar?cluster=15922805360081593111&hl=en&as_sdt=0,5
| 3 | 2,022 |
Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
| 11 |
neurips
| 3 | 0 |
2023-06-16 22:59:55.731000
|
https://github.com/jpthu17/emcl
| 34 |
Expectation-maximization contrastive learning for compact video-and-language representations
|
https://scholar.google.com/scholar?cluster=11969840580847474339&hl=en&as_sdt=0,33
| 3 | 2,022 |
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:55.944000
|
https://github.com/runame/laplace-refinement
| 7 |
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
|
https://scholar.google.com/scholar?cluster=9536243879108520698&hl=en&as_sdt=0,44
| 1 | 2,022 |
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:59:56.155000
|
https://github.com/kiarashza/graphvae-mm
| 3 |
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
|
https://scholar.google.com/scholar?cluster=13109008245041775500&hl=en&as_sdt=0,5
| 1 | 2,022 |
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning
| 22 |
neurips
| 2 | 0 |
2023-06-16 22:59:56.368000
|
https://github.com/xiye17/textualexplincontext
| 7 |
The unreliability of explanations in few-shot prompting for textual reasoning
|
https://scholar.google.com/scholar?cluster=10734606259015724525&hl=en&as_sdt=0,36
| 1 | 2,022 |
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:56.581000
|
https://github.com/kakaobrain/leco
| 3 |
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
|
https://scholar.google.com/scholar?cluster=2977860683890519748&hl=en&as_sdt=0,18
| 4 | 2,022 |
Generalised Implicit Neural Representations
| 5 |
neurips
| 4 | 0 |
2023-06-16 22:59:56.793000
|
https://github.com/danielegrattarola/ginr
| 57 |
Generalised Implicit Neural Representations
|
https://scholar.google.com/scholar?cluster=8630199693995819513&hl=en&as_sdt=0,47
| 1 | 2,022 |
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:59:57.006000
|
https://github.com/ennisthemennis/sparse-combo-net
| 3 |
RNNs of RNNs: Recursive construction of stable assemblies of recurrent neural networks
|
https://scholar.google.com/scholar?cluster=6568419832870731540&hl=en&as_sdt=0,10
| 1 | 2,022 |
Efficient Non-Parametric Optimizer Search for Diverse Tasks
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:59:57.217000
|
https://github.com/ruocwang/efficient-optimizer-search
| 5 |
Efficient Non-Parametric Optimizer Search for Diverse Tasks
|
https://scholar.google.com/scholar?cluster=1101981355374817614&hl=en&as_sdt=0,14
| 2 | 2,022 |
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
| 42 |
neurips
| 12 | 0 |
2023-06-16 22:59:57.430000
|
https://github.com/dtsip/in-context-learning
| 87 |
What can transformers learn in-context? a case study of simple function classes
|
https://scholar.google.com/scholar?cluster=11860366070256877583&hl=en&as_sdt=0,44
| 3 | 2,022 |
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
| 19 |
neurips
| 1,839 | 124 |
2023-06-16 22:59:57.643000
|
https://github.com/sczhou/codeformer
| 8,639 |
Towards robust blind face restoration with codebook lookup transformer
|
https://scholar.google.com/scholar?cluster=7620815108092344146&hl=en&as_sdt=0,29
| 236 | 2,022 |
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:57.854000
|
https://github.com/aair-lab/grapl
| 4 |
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
|
https://scholar.google.com/scholar?cluster=4265468888207305535&hl=en&as_sdt=0,5
| 3 | 2,022 |
Information-Theoretic Safe Exploration with Gaussian Processes
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:59:58.066000
|
https://github.com/boschresearch/information-theoretic-safe-exploration
| 0 |
Information-Theoretic Safe Exploration with Gaussian Processes
|
https://scholar.google.com/scholar?cluster=14061812239298858431&hl=en&as_sdt=0,5
| 3 | 2,022 |
Instance-based Learning for Knowledge Base Completion
| 1 |
neurips
| 3 | 1 |
2023-06-16 22:59:58.277000
|
https://github.com/chenxran/instancebasedlearning
| 8 |
Instance-based Learning for Knowledge Base Completion
|
https://scholar.google.com/scholar?cluster=14765487766577879365&hl=en&as_sdt=0,43
| 2 | 2,022 |
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
| 8 |
neurips
| 6 | 2 |
2023-06-16 22:59:58.490000
|
https://github.com/vlar-group/ogc
| 87 |
OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds
|
https://scholar.google.com/scholar?cluster=15508509084461848604&hl=en&as_sdt=0,33
| 5 | 2,022 |
Look More but Care Less in Video Recognition
| 2 |
neurips
| 1 | 0 |
2023-06-16 22:59:58.702000
|
https://github.com/bespontaneous/afnet-pytorch
| 17 |
Look More but Care Less in Video Recognition
|
https://scholar.google.com/scholar?cluster=9829246812468140188&hl=en&as_sdt=0,5
| 2 | 2,022 |
BLOX: Macro Neural Architecture Search Benchmark and Algorithms
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:59:58.914000
|
https://github.com/samsunglabs/blox
| 16 |
BLOX: Macro neural architecture search benchmark and algorithms
|
https://scholar.google.com/scholar?cluster=14998161186597977202&hl=en&as_sdt=0,5
| 5 | 2,022 |
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
| 4 |
neurips
| 2 | 0 |
2023-06-16 22:59:59.127000
|
https://github.com/yaodongyu/tct
| 2 |
TCT: Convexifying federated learning using bootstrapped neural tangent kernels
|
https://scholar.google.com/scholar?cluster=17046807913297835630&hl=en&as_sdt=0,6
| 6 | 2,022 |
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
| 2 |
neurips
| 2 | 1 |
2023-06-16 22:59:59.338000
|
https://github.com/xuehansheng/neurhap
| 3 |
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
|
https://scholar.google.com/scholar?cluster=4924728742271479697&hl=en&as_sdt=0,5
| 2 | 2,022 |
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
| 14 |
neurips
| 4 | 5 |
2023-06-16 22:59:59.551000
|
https://github.com/Gorilla-Lab-SCUT/tango
| 117 |
Tango: Text-driven photorealistic and robust 3d stylization via lighting decomposition
|
https://scholar.google.com/scholar?cluster=5164034802871142304&hl=en&as_sdt=0,11
| 4 | 2,022 |
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach
| 15 |
neurips
| 5 | 0 |
2023-06-16 22:59:59.764000
|
https://github.com/mi-peng/sparse-sharpness-aware-minimization
| 21 |
Make sharpness-aware minimization stronger: A sparsified perturbation approach
|
https://scholar.google.com/scholar?cluster=18129366560164232465&hl=en&as_sdt=0,43
| 3 | 2,022 |
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space
| 6 |
neurips
| 0 | 0 |
2023-06-16 22:59:59.976000
|
https://github.com/elicassion/3dtrl
| 16 |
Learning viewpoint-agnostic visual representations by recovering tokens in 3D space
|
https://scholar.google.com/scholar?cluster=9274676018097824562&hl=en&as_sdt=0,5
| 6 | 2,022 |
Certifying Some Distributional Fairness with Subpopulation Decomposition
| 3 |
neurips
| 0 | 0 |
2023-06-16 23:00:00.188000
|
https://github.com/ai-secure/certified-fairness
| 3 |
Certifying some distributional fairness with subpopulation decomposition
|
https://scholar.google.com/scholar?cluster=4221362036776726241&hl=en&as_sdt=0,5
| 3 | 2,022 |
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
| 4 |
neurips
| 0 | 0 |
2023-06-16 23:00:00.404000
|
https://github.com/Benjamin-eecs/Theoretical-GMRL
| 3 |
A theoretical understanding of gradient bias in meta-reinforcement learning
|
https://scholar.google.com/scholar?cluster=9240869542719622997&hl=en&as_sdt=0,5
| 2 | 2,022 |
MAtt: A Manifold Attention Network for EEG Decoding
| 3 |
neurips
| 7 | 1 |
2023-06-16 23:00:00.619000
|
https://github.com/cecnl/matt
| 21 |
MAtt: A Manifold Attention Network for EEG Decoding
|
https://scholar.google.com/scholar?cluster=9527737114617546773&hl=en&as_sdt=0,33
| 1 | 2,022 |
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
| 0 |
neurips
| 0 | 0 |
2023-06-16 23:00:00.832000
|
https://github.com/abhrac/relational-proxies
| 6 |
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
|
https://scholar.google.com/scholar?cluster=1413072596102938227&hl=en&as_sdt=0,44
| 1 | 2,022 |
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
| 2 |
neurips
| 1 | 0 |
2023-06-16 23:00:01.045000
|
https://github.com/ninhpham/falconnlsf
| 4 |
Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search
|
https://scholar.google.com/scholar?cluster=9694551963166215273&hl=en&as_sdt=0,5
| 1 | 2,022 |
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
| 6 |
neurips
| 1 | 0 |
2023-06-16 23:00:01.309000
|
https://github.com/jieyibi/amdkd
| 19 |
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
|
https://scholar.google.com/scholar?cluster=6693564818674377475&hl=en&as_sdt=0,7
| 1 | 2,022 |
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound
| 0 |
neurips
| 0 | 0 |
2023-06-16 23:00:01.541000
|
https://github.com/vaidehi8913/burer-monteiro
| 3 |
The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound
|
https://scholar.google.com/scholar?cluster=693050207219401404&hl=en&as_sdt=0,5
| 2 | 2,022 |
MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training
| 20 |
neurips
| 21 | 6 |
2023-06-16 23:00:01.754000
|
https://github.com/nvlabs/minvis
| 242 |
Minvis: A minimal video instance segmentation framework without video-based training
|
https://scholar.google.com/scholar?cluster=9646541593785601186&hl=en&as_sdt=0,5
| 6 | 2,022 |
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