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Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion | 11 | neurips | 15 | 3 | 2023-06-16 16:08:33.820000 | https://github.com/wutong16/density_aware_chamfer_distance | 112 | Balanced chamfer distance as a comprehensive metric for point cloud completion | https://scholar.google.com/scholar?cluster=15226228858005494931&hl=en&as_sdt=0,36 | 6 | 2,021 |
Gradient-based Editing of Memory Examples for Online Task-free Continual Learning | 45 | neurips | 1 | 16 | 2023-06-16 16:08:34.021000 | https://github.com/INK-USC/GMED | 14 | Gradient-based editing of memory examples for online task-free continual learning | https://scholar.google.com/scholar?cluster=5596453218256135917&hl=en&as_sdt=0,5 | 7 | 2,021 |
Clockwork Variational Autoencoders | 31 | neurips | 8 | 2 | 2023-06-16 16:08:34.222000 | https://github.com/vaibhavsaxena11/cwvae | 40 | Clockwork variational autoencoders | https://scholar.google.com/scholar?cluster=16734321734301883406&hl=en&as_sdt=0,44 | 2 | 2,021 |
Language models enable zero-shot prediction of the effects of mutations on protein function | 156 | neurips | 419 | 54 | 2023-06-16 16:08:34.422000 | https://github.com/facebookresearch/esm | 2,083 | Language models enable zero-shot prediction of the effects of mutations on protein function | https://scholar.google.com/scholar?cluster=7905832058791782023&hl=en&as_sdt=0,33 | 58 | 2,021 |
Deep Reinforcement Learning at the Edge of the Statistical Precipice | 230 | neurips | 37 | 1 | 2023-06-16 16:08:34.622000 | https://github.com/google-research/rliable | 590 | Deep reinforcement learning at the edge of the statistical precipice | https://scholar.google.com/scholar?cluster=2097182699708093297&hl=en&as_sdt=0,19 | 11 | 2,021 |
Mind the Gap: Assessing Temporal Generalization in Neural Language Models | 35 | neurips | 2,436 | 170 | 2023-06-16 16:08:34.822000 | https://github.com/deepmind/deepmind-research | 11,904 | Mind the gap: Assessing temporal generalization in neural language models | https://scholar.google.com/scholar?cluster=5752613093594915014&hl=en&as_sdt=0,5 | 336 | 2,021 |
Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks | 14 | neurips | 0 | 0 | 2023-06-16 16:08:35.025000 | https://github.com/mbarsbey/sgd_comp_gen | 1 | Heavy tails in SGD and compressibility of overparametrized neural networks | https://scholar.google.com/scholar?cluster=2571177896093593726&hl=en&as_sdt=0,5 | 2 | 2,021 |
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | 79 | neurips | 8 | 1 | 2023-06-16 16:08:35.226000 | https://github.com/albert0147/sfda_neighbors | 56 | Exploiting the intrinsic neighborhood structure for source-free domain adaptation | https://scholar.google.com/scholar?cluster=10860760915812805775&hl=en&as_sdt=0,5 | 2 | 2,021 |
Learning with Noisy Correspondence for Cross-modal Matching | 33 | neurips | 3 | 2 | 2023-06-16 16:08:35.427000 | https://github.com/XLearning-SCU/2021-NeurIPS-NCR | 37 | Learning with noisy correspondence for cross-modal matching | https://scholar.google.com/scholar?cluster=15452038367398862205&hl=en&as_sdt=0,7 | 2 | 2,021 |
Parameter Prediction for Unseen Deep Architectures | 33 | neurips | 58 | 3 | 2023-06-16 16:08:35.628000 | https://github.com/facebookresearch/ppuda | 473 | Parameter prediction for unseen deep architectures | https://scholar.google.com/scholar?cluster=5856024017848216222&hl=en&as_sdt=0,5 | 19 | 2,021 |
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction | 89 | neurips | 24 | 5 | 2023-06-16 16:08:35.828000 | https://github.com/DeepGraphLearning/NBFNet | 152 | Neural bellman-ford networks: A general graph neural network framework for link prediction | https://scholar.google.com/scholar?cluster=1918122330889670479&hl=en&as_sdt=0,5 | 6 | 2,021 |
CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction | 8 | neurips | 0 | 0 | 2023-06-16 16:08:36.029000 | https://github.com/lebrat/CorticalFlow | 4 | Corticalflow: a diffeomorphic mesh transformer network for cortical surface reconstruction | https://scholar.google.com/scholar?cluster=15767727877386818542&hl=en&as_sdt=0,5 | 1 | 2,021 |
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression | 10 | neurips | 4 | 0 | 2023-06-16 16:08:36.234000 | https://github.com/google-research/sloe-logistic | 27 | SLOE: A faster method for statistical inference in high-dimensional logistic regression | https://scholar.google.com/scholar?cluster=1558840668295453842&hl=en&as_sdt=0,5 | 4 | 2,021 |
ELLA: Exploration through Learned Language Abstraction | 21 | neurips | 2 | 0 | 2023-06-16 16:08:36.440000 | https://github.com/Stanford-ILIAD/ELLA | 17 | Ella: Exploration through learned language abstraction | https://scholar.google.com/scholar?cluster=1927255777603103026&hl=en&as_sdt=0,36 | 5 | 2,021 |
Learning Distilled Collaboration Graph for Multi-Agent Perception | 56 | neurips | 18 | 2 | 2023-06-16 16:08:36.640000 | https://github.com/ai4ce/DiscoNet | 109 | Learning distilled collaboration graph for multi-agent perception | https://scholar.google.com/scholar?cluster=14200311259933317556&hl=en&as_sdt=0,3 | 5 | 2,021 |
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments | 18 | neurips | 3 | 2 | 2023-06-16 16:08:36.842000 | https://github.com/yycdavid/program-synthesis-guided-rl | 15 | Program synthesis guided reinforcement learning for partially observed environments | https://scholar.google.com/scholar?cluster=14513934498714825801&hl=en&as_sdt=0,3 | 1 | 2,021 |
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation | 24 | neurips | 54 | 6 | 2023-06-16 16:08:37.043000 | https://github.com/onion-liu/BlendGAN | 481 | Blendgan: Implicitly gan blending for arbitrary stylized face generation | https://scholar.google.com/scholar?cluster=4524216348356707290&hl=en&as_sdt=0,41 | 24 | 2,021 |
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models | 1 | neurips | 2 | 0 | 2023-06-16 16:08:37.247000 | https://github.com/lenarttreven/dgm | 4 | Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models | https://scholar.google.com/scholar?cluster=7201902897927895371&hl=en&as_sdt=0,5 | 3 | 2,021 |
Adjusting for Autocorrelated Errors in Neural Networks for Time Series | 14 | neurips | 24 | 0 | 2023-06-16 16:08:37.447000 | https://github.com/Daikon-Sun/AdjustAutocorrelation | 55 | Adjusting for autocorrelated errors in neural networks for time series | https://scholar.google.com/scholar?cluster=6601944845381484010&hl=en&as_sdt=0,19 | 5 | 2,021 |
A Geometric Analysis of Neural Collapse with Unconstrained Features | 61 | neurips | 7 | 2 | 2023-06-16 16:08:37.653000 | https://github.com/tding1/Neural-Collapse | 39 | A geometric analysis of neural collapse with unconstrained features | https://scholar.google.com/scholar?cluster=4057119112941072069&hl=en&as_sdt=0,33 | 3 | 2,021 |
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild | 61 | neurips | 31 | 2 | 2023-06-16 16:08:37.854000 | https://github.com/jasonyzhang/ners | 263 | NeRS: neural reflectance surfaces for sparse-view 3D reconstruction in the wild | https://scholar.google.com/scholar?cluster=14745401126644120046&hl=en&as_sdt=0,10 | 12 | 2,021 |
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning | 32 | neurips | 0 | 1 | 2023-06-16 16:08:38.055000 | https://github.com/vanint/core-tuning | 20 | Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning | https://scholar.google.com/scholar?cluster=9361339446003315812&hl=en&as_sdt=0,5 | 2 | 2,021 |
Topology-Imbalance Learning for Semi-Supervised Node Classification | 33 | neurips | 7 | 3 | 2023-06-16 16:08:38.256000 | https://github.com/victorchen96/renode | 46 | Topology-imbalance learning for semi-supervised node classification | https://scholar.google.com/scholar?cluster=13925259727682632154&hl=en&as_sdt=0,1 | 2 | 2,021 |
Gradient Inversion with Generative Image Prior | 50 | neurips | 3 | 1 | 2023-06-16 16:08:38.457000 | https://github.com/ml-postech/gradient-inversion-generative-image-prior | 24 | Gradient inversion with generative image prior | https://scholar.google.com/scholar?cluster=17804052682569498638&hl=en&as_sdt=0,21 | 3 | 2,021 |
Autobahn: Automorphism-based Graph Neural Nets | 28 | neurips | 2 | 0 | 2023-06-16 16:08:38.658000 | https://github.com/risilab/Autobahn | 26 | Autobahn: Automorphism-based graph neural nets | https://scholar.google.com/scholar?cluster=15296065143551246227&hl=en&as_sdt=0,5 | 5 | 2,021 |
Data Augmentation Can Improve Robustness | 101 | neurips | 2,436 | 170 | 2023-06-16 16:08:38.858000 | https://github.com/deepmind/deepmind-research | 11,904 | Data augmentation can improve robustness | https://scholar.google.com/scholar?cluster=12512503752375350271&hl=en&as_sdt=0,33 | 336 | 2,021 |
Deep Explicit Duration Switching Models for Time Series | 13 | neurips | 4 | 0 | 2023-06-16 16:08:39.059000 | https://github.com/abdulfatir/REDSDS | 14 | Deep explicit duration switching models for time series | https://scholar.google.com/scholar?cluster=14557842774333633153&hl=en&as_sdt=0,5 | 2 | 2,021 |
Shared Independent Component Analysis for Multi-Subject Neuroimaging | 6 | neurips | 0 | 0 | 2023-06-16 16:08:39.260000 | https://github.com/hugorichard/shica | 9 | Shared Independent Component Analysis for Multi-Subject Neuroimaging | https://scholar.google.com/scholar?cluster=7343578052852866167&hl=en&as_sdt=0,44 | 3 | 2,021 |
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects | 6 | neurips | 12 | 1 | 2023-06-16 16:08:39.462000 | https://github.com/rozumden/ShapeFromBlur | 107 | Shape from blur: Recovering textured 3d shape and motion of fast moving objects | https://scholar.google.com/scholar?cluster=13910484849922207382&hl=en&as_sdt=0,33 | 4 | 2,021 |
Residual Pathway Priors for Soft Equivariance Constraints | 16 | neurips | 0 | 2 | 2023-06-16 16:08:39.663000 | https://github.com/mfinzi/residual-pathway-priors | 14 | Residual pathway priors for soft equivariance constraints | https://scholar.google.com/scholar?cluster=14878562091868847850&hl=en&as_sdt=0,33 | 2 | 2,021 |
Learning Large Neighborhood Search Policy for Integer Programming | 12 | neurips | 3 | 1 | 2023-06-16 16:08:39.868000 | https://github.com/wxy1427/learn-lns-policy | 14 | Learning large neighborhood search policy for integer programming | https://scholar.google.com/scholar?cluster=16588835717125760391&hl=en&as_sdt=0,33 | 2 | 2,021 |
Provable Representation Learning for Imitation with Contrastive Fourier Features | 26 | neurips | 7,321 | 1,026 | 2023-06-16 16:08:40.068000 | https://github.com/google-research/google-research | 29,786 | Provable representation learning for imitation with contrastive fourier features | https://scholar.google.com/scholar?cluster=8157207826137904117&hl=en&as_sdt=0,26 | 727 | 2,021 |
Counterfactual Explanations in Sequential Decision Making Under Uncertainty | 18 | neurips | 3 | 0 | 2023-06-16 16:08:40.269000 | https://github.com/networks-learning/counterfactual-explanations-mdp | 10 | Counterfactual explanations in sequential decision making under uncertainty | https://scholar.google.com/scholar?cluster=12617016944988481192&hl=en&as_sdt=0,10 | 2 | 2,021 |
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness | 17 | neurips | 3 | 0 | 2023-06-16 16:08:40.470000 | https://github.com/jh-jeong/smoothmix | 18 | Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness | https://scholar.google.com/scholar?cluster=2235240635330767821&hl=en&as_sdt=0,36 | 1 | 2,021 |
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks | 8 | neurips | 0 | 0 | 2023-06-16 16:08:40.673000 | https://github.com/boschresearch/meta-rs | 7 | Meta-learning the search distribution of black-box random search based adversarial attacks | https://scholar.google.com/scholar?cluster=12889184094217530949&hl=en&as_sdt=0,5 | 5 | 2,021 |
Rectangular Flows for Manifold Learning | 24 | neurips | 1 | 1 | 2023-06-16 16:08:40.874000 | https://github.com/layer6ai-labs/rectangular-flows | 6 | Rectangular flows for manifold learning | https://scholar.google.com/scholar?cluster=10070884240732208071&hl=en&as_sdt=0,8 | 4 | 2,021 |
On the Generative Utility of Cyclic Conditionals | 1 | neurips | 7 | 1 | 2023-06-16 16:08:41.079000 | https://github.com/changliu00/cygen | 44 | On the generative utility of cyclic conditionals | https://scholar.google.com/scholar?cluster=16459389015391413710&hl=en&as_sdt=0,23 | 9 | 2,021 |
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels | 37 | neurips | 1 | 0 | 2023-06-16 16:08:41.280000 | https://github.com/erikenglesson/gjs | 16 | Generalized jensen-shannon divergence loss for learning with noisy labels | https://scholar.google.com/scholar?cluster=14179695996413180324&hl=en&as_sdt=0,14 | 1 | 2,021 |
Continual Learning via Local Module Composition | 31 | neurips | 3 | 0 | 2023-06-16 16:08:41.480000 | https://github.com/oleksost/lmc | 21 | Continual learning via local module composition | https://scholar.google.com/scholar?cluster=7775292558659449750&hl=en&as_sdt=0,18 | 1 | 2,021 |
Adversarial Examples Make Strong Poisons | 53 | neurips | 9 | 0 | 2023-06-16 16:08:41.681000 | https://github.com/lhfowl/adversarial_poisons | 38 | Adversarial examples make strong poisons | https://scholar.google.com/scholar?cluster=14707000567139585913&hl=en&as_sdt=0,32 | 1 | 2,021 |
Coresets for Decision Trees of Signals | 12 | neurips | 1 | 0 | 2023-06-16 16:08:41.883000 | https://github.com/ernestosanches/decision-trees-coreset | 3 | Coresets for decision trees of signals | https://scholar.google.com/scholar?cluster=8121919874821938952&hl=en&as_sdt=0,31 | 1 | 2,021 |
Local plasticity rules can learn deep representations using self-supervised contrastive predictions | 27 | neurips | 2 | 0 | 2023-06-16 16:08:42.084000 | https://github.com/EPFL-LCN/pub-illing2021-neurips | 17 | Local plasticity rules can learn deep representations using self-supervised contrastive predictions | https://scholar.google.com/scholar?cluster=8723626128481871858&hl=en&as_sdt=0,37 | 6 | 2,021 |
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning | 10 | neurips | 1 | 1 | 2023-06-16 16:08:42.286000 | https://github.com/VivienCabannes/partial_labelling | 9 | Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning | https://scholar.google.com/scholar?cluster=14486030124977090010&hl=en&as_sdt=0,10 | 1 | 2,021 |
Unlabeled Principal Component Analysis | 6 | neurips | 1 | 0 | 2023-06-16 16:08:42.486000 | https://github.com/yaoyzh/Unlabeled_PCA_NeurIPS2021 | 1 | Unlabeled principal component analysis | https://scholar.google.com/scholar?cluster=13930442209235067345&hl=en&as_sdt=0,1 | 1 | 2,021 |
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data | 12 | neurips | 1 | 0 | 2023-06-16 16:08:42.687000 | https://github.com/oatml/causal-bald | 13 | Causal-bald: Deep bayesian active learning of outcomes to infer treatment-effects from observational data | https://scholar.google.com/scholar?cluster=14293468675130337012&hl=en&as_sdt=0,14 | 0 | 2,021 |
Scalable Rule-Based Representation Learning for Interpretable Classification | 29 | neurips | 13 | 2 | 2023-06-16 16:08:42.888000 | https://github.com/12wang3/rrl | 65 | Scalable rule-based representation learning for interpretable classification | https://scholar.google.com/scholar?cluster=4256640870246033381&hl=en&as_sdt=0,10 | 4 | 2,021 |
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection | 10 | neurips | 2 | 2 | 2023-06-16 16:08:43.090000 | https://github.com/dongnana777/bridging-non-co-occurrence | 7 | Bridging non co-occurrence with unlabeled in-the-wild data for incremental object detection | https://scholar.google.com/scholar?cluster=9835506167976269253&hl=en&as_sdt=0,33 | 2 | 2,021 |
Generating Datasets of 3D Garments with Sewing Patterns | 10 | neurips | 13 | 0 | 2023-06-16 16:08:43.290000 | https://github.com/maria-korosteleva/garment-pattern-generator | 85 | Generating datasets of 3d garments with sewing patterns | https://scholar.google.com/scholar?cluster=15013601898375662673&hl=en&as_sdt=0,5 | 4 | 2,021 |
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation | 23 | neurips | 11 | 2 | 2023-06-16 16:08:43.491000 | https://github.com/stanfordmimi/skm-tea | 56 | Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation | https://scholar.google.com/scholar?cluster=14148193139570714789&hl=en&as_sdt=0,33 | 4 | 2,021 |
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee | 7 | neurips | 2 | 0 | 2023-06-16 16:08:43.693000 | https://github.com/ds3lab/feebee | 4 | Evaluating Bayes error estimators on real-world datasets with FeeBee | https://scholar.google.com/scholar?cluster=2464591974383238873&hl=en&as_sdt=0,5 | 7 | 2,021 |
PASS: An ImageNet replacement for self-supervised pretraining without humans | 23 | neurips | 17 | 2 | 2023-06-16 16:08:43.895000 | https://github.com/yukimasano/PASS | 254 | Pass: An imagenet replacement for self-supervised pretraining without humans | https://scholar.google.com/scholar?cluster=16947555364895475194&hl=en&as_sdt=0,44 | 6 | 2,021 |
URLB: Unsupervised Reinforcement Learning Benchmark | 61 | neurips | 46 | 16 | 2023-06-16 16:08:44.096000 | https://github.com/rll-research/url_benchmark | 290 | URLB: Unsupervised reinforcement learning benchmark | https://scholar.google.com/scholar?cluster=12980539145906444225&hl=en&as_sdt=0,33 | 7 | 2,021 |
An Empirical Study of Graph Contrastive Learning | 68 | neurips | 81 | 12 | 2023-06-16 16:08:44.296000 | https://github.com/GraphCL/PyGCL | 675 | An empirical study of graph contrastive learning | https://scholar.google.com/scholar?cluster=6611245938611321529&hl=en&as_sdt=0,5 | 8 | 2,021 |
Chest ImaGenome Dataset for Clinical Reasoning | 15 | neurips | 0 | 0 | 2023-06-16 16:08:44.496000 | https://github.com/LourentzouTBD/ChestImaGenomeChangeDetection | 1 | Chest ImaGenome dataset for clinical reasoning | https://scholar.google.com/scholar?cluster=3704746853609253199&hl=en&as_sdt=0,33 | 1 | 2,021 |
WRENCH: A Comprehensive Benchmark for Weak Supervision | 61 | neurips | 27 | 7 | 2023-06-16 16:08:44.697000 | https://github.com/jieyuz2/wrench | 194 | Wrench: A comprehensive benchmark for weak supervision | https://scholar.google.com/scholar?cluster=16182721416857685898&hl=en&as_sdt=0,33 | 6 | 2,021 |
A Dataset for Answering Time-Sensitive Questions | 17 | neurips | 5 | 2 | 2023-06-16 16:08:44.897000 | https://github.com/wenhuchen/time-sensitive-qa | 42 | A dataset for answering time-sensitive questions | https://scholar.google.com/scholar?cluster=9316987576931607453&hl=en&as_sdt=0,38 | 1 | 2,021 |
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation | 9 | neurips | 1 | 1 | 2023-06-16 16:08:45.097000 | https://github.com/XanderJC/medkit-learn | 22 | The medkit-learn (ing) environment: Medical decision modelling through simulation | https://scholar.google.com/scholar?cluster=17528200629661115793&hl=en&as_sdt=0,5 | 3 | 2,021 |
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics | 15 | neurips | 13 | 0 | 2023-06-16 16:08:45.298000 | https://github.com/charan223/FairDeepLearning | 31 | Benchmarking bias mitigation algorithms in representation learning through fairness metrics | https://scholar.google.com/scholar?cluster=6740709092795107376&hl=en&as_sdt=0,10 | 5 | 2,021 |
Datasets for Online Controlled Experiments | 3 | neurips | 0 | 0 | 2023-06-16 16:08:45.499000 | https://github.com/liuchbryan/oce-dataset | 4 | Datasets for online controlled experiments | https://scholar.google.com/scholar?cluster=408266997951009779&hl=en&as_sdt=0,47 | 3 | 2,021 |
The CLEAR Benchmark: Continual LEArning on Real-World Imagery | 36 | neurips | 4 | 0 | 2023-06-16 16:08:45.700000 | https://github.com/linzhiqiu/continual-learning | 13 | The clear benchmark: Continual learning on real-world imagery | https://scholar.google.com/scholar?cluster=17993292222696601191&hl=en&as_sdt=0,20 | 4 | 2,021 |
ReaSCAN: Compositional Reasoning in Language Grounding | 7 | neurips | 3 | 0 | 2023-06-16 16:08:45.903000 | https://github.com/frankaging/Reason-SCAN | 17 | ReaSCAN: Compositional reasoning in language grounding | https://scholar.google.com/scholar?cluster=7096206809179384730&hl=en&as_sdt=0,5 | 5 | 2,021 |
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions | 16 | neurips | 1 | 0 | 2023-06-16 16:08:46.104000 | https://github.com/newbeeyoung/video-corruption-robustness | 16 | Benchmarking the robustness of spatial-temporal models against corruptions | https://scholar.google.com/scholar?cluster=13559459758592949977&hl=en&as_sdt=0,33 | 3 | 2,021 |
GraphGT: Machine Learning Datasets for Graph Generation and Transformation | 32 | neurips | 7 | 2 | 2023-06-16 16:08:46.304000 | https://github.com/yuanqidu/graphgt | 51 | Graphgt: Machine learning datasets for graph generation and transformation | https://scholar.google.com/scholar?cluster=11012021022689991240&hl=en&as_sdt=0,10 | 2 | 2,021 |
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation | 38 | neurips | 75 | 23 | 2023-06-16 16:08:46.505000 | https://github.com/st-tech/zr-obp | 549 | Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation | https://scholar.google.com/scholar?cluster=10707722556009377278&hl=en&as_sdt=0,36 | 88 | 2,021 |
Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI | 88 | neurips | 9 | 2 | 2023-06-16 16:08:46.706000 | https://github.com/facebookresearch/habitat-matterport3d-dataset | 91 | Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai | https://scholar.google.com/scholar?cluster=16347568328896129172&hl=en&as_sdt=0,5 | 8 | 2,021 |
A realistic approach to generate masked faces applied on two novel masked face recognition data sets | 11 | neurips | 3 | 3 | 2023-06-16 16:08:46.906000 | https://github.com/securifai/masked_faces | 28 | A realistic approach to generate masked faces applied on two novel masked face recognition data sets | https://scholar.google.com/scholar?cluster=6898524933140941644&hl=en&as_sdt=0,14 | 3 | 2,021 |
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark | 15 | neurips | 3 | 3 | 2023-06-16 16:08:47.107000 | https://github.com/mlii0117/FFA-IR | 32 | Ffa-ir: Towards an explainable and reliable medical report generation benchmark | https://scholar.google.com/scholar?cluster=6645019312139456748&hl=en&as_sdt=0,39 | 1 | 2,021 |
What Would Jiminy Cricket Do? Towards Agents That Behave Morally | 14 | neurips | 3 | 0 | 2023-06-16 16:08:47.309000 | https://github.com/hendrycks/jiminy-cricket | 19 | What would jiminy cricket do? Towards agents that behave morally | https://scholar.google.com/scholar?cluster=14711980494808596715&hl=en&as_sdt=0,5 | 2 | 2,021 |
Programming Puzzles | 16 | neurips | 88 | 20 | 2023-06-16 16:08:47.509000 | https://github.com/microsoft/PythonProgrammingPuzzles | 880 | Programming puzzles | https://scholar.google.com/scholar?cluster=5425926029419561217&hl=en&as_sdt=0,23 | 16 | 2,021 |
An Extensible Benchmark Suite for Learning to Simulate Physical Systems | 7 | neurips | 2 | 0 | 2023-06-16 16:08:47.708000 | https://github.com/karlotness/nn-benchmark | 17 | An extensible benchmark suite for learning to simulate physical systems | https://scholar.google.com/scholar?cluster=3662433208653304264&hl=en&as_sdt=0,5 | 7 | 2,021 |
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting | 101 | neurips | 55 | 15 | 2023-06-16 16:08:47.920000 | https://github.com/argoverse/av2-api | 216 | Argoverse 2: Next generation datasets for self-driving perception and forecasting | https://scholar.google.com/scholar?cluster=650026435189304623&hl=en&as_sdt=0,5 | 10 | 2,021 |
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development | 113 | neurips | 145 | 29 | 2023-06-16 16:08:48.121000 | https://github.com/mims-harvard/TDC | 823 | Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development | https://scholar.google.com/scholar?cluster=263016632375932982&hl=en&as_sdt=0,14 | 22 | 2,021 |
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation | 75 | neurips | 39 | 26 | 2023-06-16 16:08:48.321000 | https://github.com/Junjue-Wang/LoveDA | 226 | LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation | https://scholar.google.com/scholar?cluster=7895763680437166641&hl=en&as_sdt=0,5 | 4 | 2,021 |
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge | 19 | neurips | 3 | 1 | 2023-06-16 16:08:48.521000 | https://github.com/yasumasaonoe/creak | 16 | CREAK: A dataset for commonsense reasoning over entity knowledge | https://scholar.google.com/scholar?cluster=16825406718835983392&hl=en&as_sdt=0,4 | 3 | 2,021 |
A Large-Scale Database for Graph Representation Learning | 29 | neurips | 11 | 2 | 2023-06-16 16:08:48.721000 | https://github.com/safreita1/malnet-graph | 35 | A large-scale database for graph representation learning | https://scholar.google.com/scholar?cluster=10177352581940453815&hl=en&as_sdt=0,11 | 2 | 2,021 |
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling | 32 | neurips | 1 | 1 | 2023-06-16 16:08:48.921000 | https://github.com/HLTCHKUST/BiToD | 21 | Bitod: A bilingual multi-domain dataset for task-oriented dialogue modeling | https://scholar.google.com/scholar?cluster=3554059482240566542&hl=en&as_sdt=0,5 | 5 | 2,021 |
HumBugDB: A Large-scale Acoustic Mosquito Dataset | 16 | neurips | 9 | 0 | 2023-06-16 16:08:49.122000 | https://github.com/humbug-mosquito/humbugdb | 32 | HumBugDB: a large-scale acoustic mosquito dataset | https://scholar.google.com/scholar?cluster=16288671162507786903&hl=en&as_sdt=0,10 | 6 | 2,021 |
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data | 32 | neurips | 52 | 13 | 2023-06-16 16:08:49.322000 | https://github.com/apple/ARKitScenes | 476 | ARKitScenes--A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data | https://scholar.google.com/scholar?cluster=16950635420621153680&hl=en&as_sdt=0,10 | 25 | 2,021 |
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information | 68 | neurips | 18 | 5 | 2023-06-16 16:08:49.523000 | https://github.com/Raldir/FEVEROUS | 54 | Feverous: Fact extraction and verification over unstructured and structured information | https://scholar.google.com/scholar?cluster=5675725561486450622&hl=en&as_sdt=0,47 | 2 | 2,021 |
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning | 22 | neurips | 15 | 4 | 2023-06-16 16:08:49.723000 | https://github.com/thudm/grb | 77 | Graph robustness benchmark: Benchmarking the adversarial robustness of graph machine learning | https://scholar.google.com/scholar?cluster=740832455944731540&hl=en&as_sdt=0,18 | 8 | 2,021 |
CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review | 55 | neurips | 95 | 9 | 2023-06-16 16:08:49.924000 | https://github.com/TheAtticusProject/cuad | 302 | Cuad: An expert-annotated nlp dataset for legal contract review | https://scholar.google.com/scholar?cluster=9100258365947035090&hl=en&as_sdt=0,5 | 13 | 2,021 |
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation | 165 | neurips | 68 | 20 | 2023-06-16 16:08:50.124000 | https://github.com/threedworld-mit/tdw | 388 | Threedworld: A platform for interactive multi-modal physical simulation | https://scholar.google.com/scholar?cluster=7060550992548001632&hl=en&as_sdt=0,5 | 21 | 2,021 |
Personalized Benchmarking with the Ludwig Benchmarking Toolkit | 11 | neurips | 1,046 | 279 | 2023-06-16 16:08:50.324000 | https://github.com/ludwig-ai/ludwig | 8,974 | Personalized benchmarking with the ludwig benchmarking toolkit | https://scholar.google.com/scholar?cluster=604774687945155345&hl=en&as_sdt=0,39 | 186 | 2,021 |
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs | 10 | neurips | 4 | 1 | 2023-06-16 16:08:50.524000 | https://github.com/hkust-knowcomp/efo-1-qa-benchmark | 17 | Benchmarking the combinatorial generalizability of complex query answering on knowledge graphs | https://scholar.google.com/scholar?cluster=14710134969550409295&hl=en&as_sdt=0,34 | 2 | 2,021 |
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions | 18 | neurips | 7 | 0 | 2023-06-16 16:08:50.724000 | https://github.com/neuroethology/TREBA | 66 | The multi-agent behavior dataset: Mouse dyadic social interactions | https://scholar.google.com/scholar?cluster=17767650578818476506&hl=en&as_sdt=0,11 | 3 | 2,021 |
DABS: a Domain-Agnostic Benchmark for Self-Supervised Learning | 22 | neurips | 11 | 0 | 2023-06-16 16:08:50.925000 | https://github.com/alextamkin/dabs | 92 | DABS: A domain-agnostic benchmark for self-supervised learning | https://scholar.google.com/scholar?cluster=6831578764269382202&hl=en&as_sdt=0,33 | 3 | 2,021 |
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning | 21 | neurips | 13 | 0 | 2023-06-16 16:08:51.125000 | https://github.com/dido1998/CausalMBRL | 37 | Systematic evaluation of causal discovery in visual model based reinforcement learning | https://scholar.google.com/scholar?cluster=10762852414986189275&hl=en&as_sdt=0,33 | 5 | 2,021 |
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning | 23 | neurips | 17 | 9 | 2023-06-16 16:08:51.325000 | https://github.com/sustainlab-group/sustainbench | 76 | Sustainbench: Benchmarks for monitoring the sustainable development goals with machine learning | https://scholar.google.com/scholar?cluster=11548079407766263618&hl=en&as_sdt=0,33 | 5 | 2,021 |
STEP: Segmenting and Tracking Every Pixel | 41 | neurips | 156 | 30 | 2023-06-16 16:08:51.526000 | https://github.com/google-research/deeplab2 | 906 | Step: Segmenting and tracking every pixel | https://scholar.google.com/scholar?cluster=3403854428676887512&hl=en&as_sdt=0,5 | 23 | 2,021 |
KLUE: Korean Language Understanding Evaluation | 133 | neurips | 57 | 16 | 2023-06-16 16:08:51.727000 | https://github.com/KLUE-benchmark/KLUE | 505 | Klue: Korean language understanding evaluation | https://scholar.google.com/scholar?cluster=12921581347443932322&hl=en&as_sdt=0,33 | 19 | 2,021 |
ImageNet-21K Pretraining for the Masses | 239 | neurips | 65 | 13 | 2023-06-16 16:08:51.927000 | https://github.com/Alibaba-MIIL/ImageNet21K | 629 | Imagenet-21k pretraining for the masses | https://scholar.google.com/scholar?cluster=15637978761893120373&hl=en&as_sdt=0,33 | 10 | 2,021 |
Benchmarking Multimodal AutoML for Tabular Data with Text Fields | 16 | neurips | 6 | 1 | 2023-06-16 16:08:52.140000 | https://github.com/sxjscience/automl_multimodal_benchmark | 47 | Benchmarking multimodal automl for tabular data with text fields | https://scholar.google.com/scholar?cluster=15129006949053475475&hl=en&as_sdt=0,50 | 7 | 2,021 |
EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction | 17 | neurips | 6 | 1 | 2023-06-16 16:08:52.340000 | https://github.com/ardkastrati/eegeyenet | 26 | EEGEyeNet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction | https://scholar.google.com/scholar?cluster=18415629137722917831&hl=en&as_sdt=0,33 | 3 | 2,021 |
RobustBench: a standardized adversarial robustness benchmark | 316 | neurips | 78 | 2 | 2023-06-16 16:08:52.540000 | https://github.com/RobustBench/robustbench | 476 | Robustbench: a standardized adversarial robustness benchmark | https://scholar.google.com/scholar?cluster=2257115641228924434&hl=en&as_sdt=0,14 | 9 | 2,021 |
EventNarrative: A Large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation | 8 | neurips | 0 | 0 | 2023-06-16 16:08:52.740000 | https://github.com/acolas1/EventNarrative | 4 | EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation | https://scholar.google.com/scholar?cluster=9691193925909218204&hl=en&as_sdt=0,7 | 1 | 2,021 |
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents | 11 | neurips | 19 | 1 | 2023-06-16 16:08:52.951000 | https://github.com/deepmind/dm_alchemy | 191 | Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents | https://scholar.google.com/scholar?cluster=16056252909542082648&hl=en&as_sdt=0,33 | 15 | 2,021 |
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks | 60 | neurips | 180 | 2 | 2023-06-16 16:08:53.156000 | https://github.com/IBM/Project_CodeNet | 1,361 | CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks | https://scholar.google.com/scholar?cluster=9700363462544607592&hl=en&as_sdt=0,43 | 53 | 2,021 |
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation | 62 | neurips | 5 | 3 | 2023-06-16 16:08:53.365000 | https://github.com/VALUE-Leaderboard/StarterCode | 80 | Value: A multi-task benchmark for video-and-language understanding evaluation | https://scholar.google.com/scholar?cluster=3360639722012536549&hl=en&as_sdt=0,48 | 4 | 2,021 |
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks | 67 | neurips | 49 | 6 | 2023-06-16 16:08:53.574000 | https://github.com/yandex-research/shifts | 207 | Shifts: A dataset of real distributional shift across multiple large-scale tasks | https://scholar.google.com/scholar?cluster=6919306211316072115&hl=en&as_sdt=0,33 | 14 | 2,021 |
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms | 50 | neurips | 54 | 17 | 2023-06-16 16:08:53.775000 | https://github.com/indyfree/CARLA | 239 | Carla: a python library to benchmark algorithmic recourse and counterfactual explanation algorithms | https://scholar.google.com/scholar?cluster=319623159508225394&hl=en&as_sdt=0,14 | 6 | 2,021 |
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