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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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