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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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