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Measuring Mathematical Problem Solving With the MATH Dataset
| 115 |
neurips
| 40 | 2 |
2023-06-16 16:08:53.975000
|
https://github.com/hendrycks/math
| 382 |
Measuring mathematical problem solving with the math dataset
|
https://scholar.google.com/scholar?cluster=15840802134856527968&hl=en&as_sdt=0,33
| 10 | 2,021 |
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
| 25 |
neurips
| 12 | 1 |
2023-06-16 16:08:54.175000
|
https://github.com/abacusai/xai-bench
| 38 |
Synthetic benchmarks for scientific research in explainable machine learning
|
https://scholar.google.com/scholar?cluster=16562504409000765600&hl=en&as_sdt=0,7
| 7 | 2,021 |
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
| 305 |
neurips
| 313 | 23 |
2023-06-16 16:08:54.376000
|
https://github.com/microsoft/CodeXGLUE
| 1,143 |
Codexglue: A machine learning benchmark dataset for code understanding and generation
|
https://scholar.google.com/scholar?cluster=3348257757676709546&hl=en&as_sdt=0,34
| 34 | 2,021 |
MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
| 23 |
neurips
| 116 | 23 |
2023-06-16 16:08:54.577000
|
https://github.com/modeltc/mqbench
| 586 |
MQBench: Towards reproducible and deployable model quantization benchmark
|
https://scholar.google.com/scholar?cluster=3991463510006314628&hl=en&as_sdt=0,33
| 15 | 2,021 |
Measuring Coding Challenge Competence With APPS
| 101 |
neurips
| 38 | 5 |
2023-06-16 16:08:54.778000
|
https://github.com/hendrycks/apps
| 274 |
Measuring coding challenge competence with apps
|
https://scholar.google.com/scholar?cluster=17541608988106931861&hl=en&as_sdt=0,5
| 12 | 2,021 |
ATOM3D: Tasks on Molecules in Three Dimensions
| 62 |
neurips
| 32 | 18 |
2023-06-16 16:08:54.981000
|
https://github.com/drorlab/atom3d
| 249 |
Atom3d: Tasks on molecules in three dimensions
|
https://scholar.google.com/scholar?cluster=8766868616148993451&hl=en&as_sdt=0,5
| 14 | 2,021 |
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
| 32 |
neurips
| 6 | 0 |
2023-06-16 16:08:55.188000
|
https://github.com/rub-syssec/wavefake
| 42 |
Wavefake: A data set to facilitate audio deepfake detection
|
https://scholar.google.com/scholar?cluster=6599528507595040003&hl=en&as_sdt=0,39
| 6 | 2,021 |
RAFT: A Real-World Few-Shot Text Classification Benchmark
| 23 |
neurips
| 10 | 1 |
2023-06-16 16:08:55.389000
|
https://github.com/oughtinc/raft-baselines
| 11 |
RAFT: A real-world few-shot text classification benchmark
|
https://scholar.google.com/scholar?cluster=14991051401140095655&hl=en&as_sdt=0,14
| 2 | 2,021 |
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
| 25 |
neurips
| 2 | 12 |
2023-06-16 16:08:55.589000
|
https://github.com/cogtoolslab/physics-benchmarking-neurips2021
| 44 |
Physion: Evaluating physical prediction from vision in humans and machines
|
https://scholar.google.com/scholar?cluster=8733318111076645893&hl=en&as_sdt=0,5
| 9 | 2,021 |
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
| 17 |
neurips
| 13 | 0 |
2023-06-16 16:08:55.790000
|
https://github.com/lupantech/iconqa
| 31 |
Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
|
https://scholar.google.com/scholar?cluster=6611908787102909279&hl=en&as_sdt=0,5
| 3 | 2,021 |
SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation
| 42 |
neurips
| 4 | 0 |
2023-06-16 16:08:55.992000
|
https://github.com/segmentmeifyoucan/road-anomaly-benchmark
| 20 |
Segmentmeifyoucan: A benchmark for anomaly segmentation
|
https://scholar.google.com/scholar?cluster=402806083575370360&hl=en&as_sdt=0,21
| 0 | 2,021 |
B-Pref: Benchmarking Preference-Based Reinforcement Learning
| 29 |
neurips
| 17 | 6 |
2023-06-16 16:08:56.192000
|
https://github.com/rll-research/b-pref
| 76 |
B-pref: Benchmarking preference-based reinforcement learning
|
https://scholar.google.com/scholar?cluster=13266882268362659539&hl=en&as_sdt=0,33
| 0 | 2,021 |
NaturalProofs: Mathematical Theorem Proving in Natural Language
| 22 |
neurips
| 6 | 0 |
2023-06-16 16:08:56.392000
|
https://github.com/wellecks/naturalproofs
| 90 |
Naturalproofs: Mathematical theorem proving in natural language
|
https://scholar.google.com/scholar?cluster=955828414616536580&hl=en&as_sdt=0,32
| 7 | 2,021 |
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
| 165 |
neurips
| 397 | 17 |
2023-06-16 16:08:56.592000
|
https://github.com/snap-stanford/ogb
| 1,685 |
Ogb-lsc: A large-scale challenge for machine learning on graphs
|
https://scholar.google.com/scholar?cluster=15358624115412194871&hl=en&as_sdt=0,33
| 42 | 2,021 |
An Information Retrieval Approach to Building Datasets for Hate Speech Detection
| 12 |
neurips
| 1 | 0 |
2023-06-16 16:08:56.793000
|
https://github.com/mdmustafizurrahman/An-Information-Retrieval-Approach-to-Building-Datasets-for-Hate-Speech-Detection
| 6 |
An information retrieval approach to building datasets for hate speech detection
|
https://scholar.google.com/scholar?cluster=8624990227295438686&hl=en&as_sdt=0,33
| 3 | 2,021 |
RedCaps: Web-curated image-text data created by the people, for the people
| 54 |
neurips
| 7 | 0 |
2023-06-16 16:08:56.993000
|
https://github.com/redcaps-dataset/redcaps-downloader
| 34 |
Redcaps: Web-curated image-text data created by the people, for the people
|
https://scholar.google.com/scholar?cluster=16709143259160494609&hl=en&as_sdt=0,33
| 1 | 2,021 |
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
| 35 |
neurips
| 2 | 0 |
2023-06-16 16:08:57.193000
|
https://github.com/karazijal/clevrtex-generation
| 30 |
Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation
|
https://scholar.google.com/scholar?cluster=13383231498167855057&hl=en&as_sdt=0,37
| 2 | 2,021 |
A Channel Coding Benchmark for Meta-Learning
| 6 |
neurips
| 1 | 0 |
2023-06-16 16:08:57.393000
|
https://github.com/ruihuili/MetaCC
| 8 |
A channel coding benchmark for meta-learning
|
https://scholar.google.com/scholar?cluster=1943764158077040305&hl=en&as_sdt=0,18
| 5 | 2,021 |
Chaos as an interpretable benchmark for forecasting and data-driven modelling
| 31 |
neurips
| 26 | 0 |
2023-06-16 16:08:57.594000
|
https://github.com/williamgilpin/dysts
| 204 |
Chaos as an interpretable benchmark for forecasting and data-driven modelling
|
https://scholar.google.com/scholar?cluster=10113442544337188110&hl=en&as_sdt=0,33
| 7 | 2,021 |
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
| 15 |
neurips
| 3 | 1 |
2023-06-16 16:08:57.794000
|
https://github.com/releaunifreiburg/HPO-B
| 17 |
HPO-B: A large-scale reproducible benchmark for black-box HPO based on OpenML
|
https://scholar.google.com/scholar?cluster=7650782388880150578&hl=en&as_sdt=0,39
| 2 | 2,021 |
Monash Time Series Forecasting Archive
| 42 |
neurips
| 34 | 0 |
2023-06-16 16:08:57.994000
|
https://github.com/rakshitha123/TSForecasting
| 103 |
Monash time series forecasting archive
|
https://scholar.google.com/scholar?cluster=2787747679550330203&hl=en&as_sdt=0,31
| 6 | 2,021 |
Which priors matter? Benchmarking models for learning latent dynamics
| 16 |
neurips
| 6 | 2 |
2023-06-16 16:08:58.195000
|
https://github.com/deepmind/dm_hamiltonian_dynamics_suite
| 28 |
Which priors matter? Benchmarking models for learning latent dynamics
|
https://scholar.google.com/scholar?cluster=377030899492556244&hl=en&as_sdt=0,1
| 5 | 2,021 |
Benchmarks for Corruption Invariant Person Re-identification
| 11 |
neurips
| 17 | 2 |
2023-06-16 16:08:58.395000
|
https://github.com/MinghuiChen43/CIL-ReID
| 78 |
Benchmarks for corruption invariant person re-identification
|
https://scholar.google.com/scholar?cluster=13448668385156906300&hl=en&as_sdt=0,10
| 4 | 2,021 |
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
| 4 |
neurips
| 4 | 0 |
2023-06-16 16:08:58.595000
|
https://github.com/RolnickLab/climart
| 32 |
ClimART: A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models
|
https://scholar.google.com/scholar?cluster=15949022047670845408&hl=en&as_sdt=0,41
| 2 | 2,021 |
Variance-Aware Machine Translation Test Sets
| 1 |
neurips
| 0 | 0 |
2023-06-16 16:08:58.796000
|
https://github.com/nlp2ct/variance-aware-mt-test-sets
| 6 |
Variance-aware machine translation test sets
|
https://scholar.google.com/scholar?cluster=101231479911651461&hl=en&as_sdt=0,10
| 2 | 2,021 |
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
| 44 |
neurips
| 44 | 2 |
2023-06-16 16:08:58.996000
|
https://github.com/facebookresearch/minihack
| 383 |
Minihack the planet: A sandbox for open-ended reinforcement learning research
|
https://scholar.google.com/scholar?cluster=6630578925704373127&hl=en&as_sdt=0,5
| 11 | 2,021 |
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
| 8 |
neurips
| 2 | 1 |
2023-06-16 22:56:56.096000
|
https://github.com/thumnlab/nas-bench-graph
| 12 |
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
|
https://scholar.google.com/scholar?cluster=974156453210928124&hl=en&as_sdt=0,10
| 7 | 2,022 |
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:56:56.310000
|
https://github.com/trevorcampbell/quasi-newton-coresets-experiments
| 1 |
Fast Bayesian coresets via subsampling and quasi-Newton refinement
|
https://scholar.google.com/scholar?cluster=12514193164456670939&hl=en&as_sdt=0,31
| 1 | 2,022 |
What You See is What You Classify: Black Box Attributions
| 1 |
neurips
| 3 | 0 |
2023-06-16 22:56:56.520000
|
https://github.com/stevenstalder/nn-explainer
| 22 |
What You See is What You Classify: Black Box Attributions
|
https://scholar.google.com/scholar?cluster=7817582227897435675&hl=en&as_sdt=0,5
| 1 | 2,022 |
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
| 20 |
neurips
| 6 | 3 |
2023-06-16 22:56:56.731000
|
https://github.com/dongzelian/ssf
| 105 |
Scaling & shifting your features: A new baseline for efficient model tuning
|
https://scholar.google.com/scholar?cluster=15457903862760581709&hl=en&as_sdt=0,33
| 2 | 2,022 |
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
| 30 |
neurips
| 19 | 1 |
2023-06-16 22:56:56.941000
|
https://github.com/antoyang/FrozenBiLM
| 98 |
Zero-shot video question answering via frozen bidirectional language models
|
https://scholar.google.com/scholar?cluster=14506268695911835029&hl=en&as_sdt=0,44
| 4 | 2,022 |
Using natural language and program abstractions to instill human inductive biases in machines
| 5 |
neurips
| 3 | 0 |
2023-06-16 22:56:57.151000
|
https://github.com/sreejank/language_and_programs
| 4 |
Using natural language and program abstractions to instill human inductive biases in machines
|
https://scholar.google.com/scholar?cluster=18321817709222277184&hl=en&as_sdt=0,44
| 1 | 2,022 |
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
| 0 |
neurips
| 1 | 1 |
2023-06-16 22:56:57.362000
|
https://github.com/hci-unihd/branchedot
| 3 |
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
|
https://scholar.google.com/scholar?cluster=2014031354721865805&hl=en&as_sdt=0,21
| 1 | 2,022 |
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
| 0 |
neurips
| 1 | 0 |
2023-06-16 22:56:57.573000
|
https://github.com/niopeng/CHIMLE
| 3 |
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
|
https://scholar.google.com/scholar?cluster=6104344160615943312&hl=en&as_sdt=0,5
| 2 | 2,022 |
Diffusion Visual Counterfactual Explanations
| 6 |
neurips
| 5 | 1 |
2023-06-16 22:56:57.784000
|
https://github.com/valentyn1boreiko/dvces
| 22 |
Diffusion visual counterfactual explanations
|
https://scholar.google.com/scholar?cluster=10867197549616618589&hl=en&as_sdt=0,5
| 2 | 2,022 |
Recurrent Video Restoration Transformer with Guided Deformable Attention
| 17 |
neurips
| 17 | 15 |
2023-06-16 22:56:57.996000
|
https://github.com/jingyunliang/rvrt
| 216 |
Recurrent video restoration transformer with guided deformable attention
|
https://scholar.google.com/scholar?cluster=11993953591906088344&hl=en&as_sdt=0,1
| 22 | 2,022 |
On-Demand Sampling: Learning Optimally from Multiple Distributions
| 5 |
neurips
| 1 | 0 |
2023-06-16 22:56:58.207000
|
https://github.com/ericzhao28/multidistributionlearning
| 7 |
On-demand sampling: Learning optimally from multiple distributions
|
https://scholar.google.com/scholar?cluster=89881707711489723&hl=en&as_sdt=0,5
| 3 | 2,022 |
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays
| 9 |
neurips
| 1 | 0 |
2023-06-16 22:56:58.417000
|
https://github.com/konstmish/asynchronous_sgd
| 4 |
Asynchronous sgd beats minibatch sgd under arbitrary delays
|
https://scholar.google.com/scholar?cluster=2013363266003001191&hl=en&as_sdt=0,5
| 1 | 2,022 |
Coresets for Relational Data and The Applications
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:56:58.627000
|
https://github.com/cjx-zar/coresets-for-relational-data-and-the-applications
| 1 |
Coresets for Relational Data and The Applications
|
https://scholar.google.com/scholar?cluster=9554541870090821318&hl=en&as_sdt=0,5
| 1 | 2,022 |
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
| 33 |
neurips
| 9 | 1 |
2023-06-16 22:56:58.838000
|
https://github.com/yumeng5/supergen
| 47 |
Generating training data with language models: Towards zero-shot language understanding
|
https://scholar.google.com/scholar?cluster=14481752723663721801&hl=en&as_sdt=0,5
| 2 | 2,022 |
Robust Binary Models by Pruning Randomly-initialized Networks
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:56:59.049000
|
https://github.com/IVRL/RobustBinarySubNet
| 2 |
Robust Binary Models by Pruning Randomly-initialized Networks
|
https://scholar.google.com/scholar?cluster=4369217517871260894&hl=en&as_sdt=0,22
| 3 | 2,022 |
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:56:59.260000
|
https://github.com/lviano/identifiability_irl
| 1 |
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
|
https://scholar.google.com/scholar?cluster=14730598469172065139&hl=en&as_sdt=0,5
| 1 | 2,022 |
Efficient Knowledge Distillation from Model Checkpoints
| 6 |
neurips
| 0 | 2 |
2023-06-16 22:56:59.471000
|
https://github.com/leaplabthu/checkpointkd
| 17 |
Efficient Knowledge Distillation from Model Checkpoints
|
https://scholar.google.com/scholar?cluster=2353993256352314616&hl=en&as_sdt=0,10
| 2 | 2,022 |
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
| 9 |
neurips
| 239 | 19 |
2023-06-16 22:56:59.682000
|
https://github.com/divelab/DIG
| 1,503 |
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
|
https://scholar.google.com/scholar?cluster=1138590591357875306&hl=en&as_sdt=0,5
| 33 | 2,022 |
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:56:59.893000
|
https://github.com/jiaweihhuang/tiered-rl-experiments
| 1 |
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
|
https://scholar.google.com/scholar?cluster=7975992698003675864&hl=en&as_sdt=0,5
| 1 | 2,022 |
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:57:00.103000
|
https://github.com/gabrielvc/br_snis
| 0 |
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
|
https://scholar.google.com/scholar?cluster=18224130644100416616&hl=en&as_sdt=0,33
| 2 | 2,022 |
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:57:00.315000
|
https://github.com/wmz9/early_stage_convergence_neurips2022
| 0 |
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks
|
https://scholar.google.com/scholar?cluster=6474562401144643315&hl=en&as_sdt=0,36
| 1 | 2,022 |
On Divergence Measures for Bayesian Pseudocoresets
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:57:00.525000
|
https://github.com/balhaekim/bpc-divergences
| 6 |
On Divergence Measures for Bayesian Pseudocoresets
|
https://scholar.google.com/scholar?cluster=2002320216778529184&hl=en&as_sdt=0,33
| 1 | 2,022 |
Unsupervised Learning of Equivariant Structure from Sequences
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:00.736000
|
https://github.com/takerum/meta_sequential_prediction
| 13 |
Unsupervised Learning of Equivariant Structure from Sequences
|
https://scholar.google.com/scholar?cluster=304500116743207302&hl=en&as_sdt=0,33
| 3 | 2,022 |
DC-BENCH: Dataset Condensation Benchmark
| 13 |
neurips
| 15 | 4 |
2023-06-16 22:57:00.948000
|
https://github.com/justincui03/dc_benchmark
| 60 |
DC-BENCH: Dataset Condensation Benchmark
|
https://scholar.google.com/scholar?cluster=16210328737996830947&hl=en&as_sdt=0,31
| 3 | 2,022 |
Mask Matching Transformer for Few-Shot Segmentation
| 2 |
neurips
| 2 | 2 |
2023-06-16 22:57:01.159000
|
https://github.com/picsart-ai-research/mask-matching-transformer
| 9 |
Mask matching transformer for few-shot segmentation
|
https://scholar.google.com/scholar?cluster=10843608391275474221&hl=en&as_sdt=0,14
| 2 | 2,022 |
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:01.369000
|
https://github.com/yuqin-yang/sem-me-ur
| 3 |
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error
|
https://scholar.google.com/scholar?cluster=2946367080464939107&hl=en&as_sdt=0,5
| 1 | 2,022 |
Sparsity in Continuous-Depth Neural Networks
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:01.579000
|
https://github.com/theislab/pathreg
| 6 |
Sparsity in Continuous-Depth Neural Networks
|
https://scholar.google.com/scholar?cluster=17433656016983930477&hl=en&as_sdt=0,5
| 2 | 2,022 |
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:01.790000
|
https://github.com/point0bar1/hat-ebm
| 6 |
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM
|
https://scholar.google.com/scholar?cluster=9884499776664824848&hl=en&as_sdt=0,5
| 1 | 2,022 |
Learning Best Combination for Efficient N:M Sparsity
| 8 |
neurips
| 1 | 1 |
2023-06-16 22:57:02.001000
|
https://github.com/zyxxmu/lbc
| 12 |
Learning Best Combination for Efficient N: M Sparsity
|
https://scholar.google.com/scholar?cluster=16372091815388983729&hl=en&as_sdt=0,47
| 1 | 2,022 |
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera
| 10 |
neurips
| 56 | 2 |
2023-06-16 22:57:02.212000
|
https://github.com/USTC3DV/NDR-code
| 473 |
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera
|
https://scholar.google.com/scholar?cluster=13429723672791415144&hl=en&as_sdt=0,44
| 14 | 2,022 |
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations
| 6 |
neurips
| 4 | 0 |
2023-06-16 22:57:02.423000
|
https://github.com/ramanshsharma2806/dt-pinn
| 7 |
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations
|
https://scholar.google.com/scholar?cluster=8326898373618608697&hl=en&as_sdt=0,28
| 4 | 2,022 |
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
| 7 |
neurips
| 2 | 0 |
2023-06-16 22:57:02.634000
|
https://github.com/archanabura/dope-doublyoptimisticpessimisticexploration
| 1 |
DOPE: Doubly optimistic and pessimistic exploration for safe reinforcement learning
|
https://scholar.google.com/scholar?cluster=15050715295292728061&hl=en&as_sdt=0,5
| 1 | 2,022 |
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:02.845000
|
https://github.com/kexinjinnn/equitopo
| 7 |
Communication-Efficient Topologies for Decentralized Learning with Consensus Rate
|
https://scholar.google.com/scholar?cluster=16384367809793868682&hl=en&as_sdt=0,33
| 1 | 2,022 |
Dataset Distillation via Factorization
| 20 |
neurips
| 6 | 3 |
2023-06-16 22:57:03.055000
|
https://github.com/huage001/datasetfactorization
| 47 |
Dataset distillation via factorization
|
https://scholar.google.com/scholar?cluster=1635742164576449623&hl=en&as_sdt=0,5
| 1 | 2,022 |
A Large Scale Search Dataset for Unbiased Learning to Rank
| 8 |
neurips
| 8 | 8 |
2023-06-16 22:57:03.266000
|
https://github.com/chuxiaokai/baidu_ultr_dataset
| 51 |
A large scale search dataset for unbiased learning to rank
|
https://scholar.google.com/scholar?cluster=16787793600985661869&hl=en&as_sdt=0,33
| 5 | 2,022 |
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
| 58 |
neurips
| 66 | 20 |
2023-06-16 22:57:03.476000
|
https://github.com/visual-attention-network/segnext
| 630 |
Segnext: Rethinking convolutional attention design for semantic segmentation
|
https://scholar.google.com/scholar?cluster=761718241536208511&hl=en&as_sdt=0,47
| 6 | 2,022 |
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
| 5 |
neurips
| 1 | 0 |
2023-06-16 22:57:03.687000
|
https://github.com/cornell-relaxml/hyperdimensional-computing
| 5 |
Understanding hyperdimensional computing for parallel single-pass learning
|
https://scholar.google.com/scholar?cluster=2441954374351827630&hl=en&as_sdt=0,5
| 0 | 2,022 |
Pre-trained Adversarial Perturbations
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:57:03.898000
|
https://github.com/banyuanhao/pap
| 15 |
Pre-trained Adversarial Perturbations
|
https://scholar.google.com/scholar?cluster=1036412260609158515&hl=en&as_sdt=0,5
| 1 | 2,022 |
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:04.108000
|
https://github.com/noahcao/disentanglement_lib_med
| 6 |
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
|
https://scholar.google.com/scholar?cluster=8166223620648232228&hl=en&as_sdt=0,10
| 0 | 2,022 |
MABSplit: Faster Forest Training Using Multi-Armed Bandits
| 1 |
neurips
| 0 | 79 |
2023-06-16 22:57:04.320000
|
https://github.com/thrungroup/fastforest
| 4 |
MABSplit: Faster Forest Training Using Multi-Armed Bandits
|
https://scholar.google.com/scholar?cluster=16839682885410953737&hl=en&as_sdt=0,22
| 0 | 2,022 |
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:57:04.531000
|
https://github.com/gallego-posada/constrained_sparsity
| 6 |
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
|
https://scholar.google.com/scholar?cluster=3017868657771533183&hl=en&as_sdt=0,5
| 2 | 2,022 |
Okapi: Generalising Better by Making Statistical Matches Match
| 0 |
neurips
| 0 | 1 |
2023-06-16 22:57:04.743000
|
https://github.com/wearepal/okapi
| 5 |
Okapi: Generalising Better by Making Statistical Matches Match
|
https://scholar.google.com/scholar?cluster=14348083558003086680&hl=en&as_sdt=0,5
| 1 | 2,022 |
Revisiting Heterophily For Graph Neural Networks
| 24 |
neurips
| 5 | 0 |
2023-06-16 22:57:04.953000
|
https://github.com/SitaoLuan/ACM-GNN
| 27 |
Revisiting heterophily for graph neural networks
|
https://scholar.google.com/scholar?cluster=10728534830275344250&hl=en&as_sdt=0,5
| 5 | 2,022 |
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
| 7 |
neurips
| 297 | 60 |
2023-06-16 22:57:05.164000
|
https://github.com/microsoft/muzic
| 3,299 |
Museformer: Transformer with Fine-and Coarse-Grained Attention for Music Generation
|
https://scholar.google.com/scholar?cluster=9919738130893761480&hl=en&as_sdt=0,14
| 63 | 2,022 |
Emergent Communication: Generalization and Overfitting in Lewis Games
| 4 |
neurips
| 0 | 0 |
2023-06-16 22:57:05.375000
|
https://github.com/mathieurita/population
| 4 |
Emergent Communication: Generalization and Overfitting in Lewis Games
|
https://scholar.google.com/scholar?cluster=9098136952282832762&hl=en&as_sdt=0,5
| 2 | 2,022 |
Efficient and Effective Augmentation Strategy for Adversarial Training
| 6 |
neurips
| 1 | 0 |
2023-06-16 22:57:05.585000
|
https://github.com/val-iisc/dajat
| 13 |
Efficient and effective augmentation strategy for adversarial training
|
https://scholar.google.com/scholar?cluster=14581218917168092627&hl=en&as_sdt=0,5
| 14 | 2,022 |
Adaptive Data Debiasing through Bounded Exploration
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:05.797000
|
https://github.com/yifankevin/adaptive_data_debiasing
| 0 |
Adaptive Data Debiasing through Bounded Exploration
|
https://scholar.google.com/scholar?cluster=6378226570310143908&hl=en&as_sdt=0,40
| 1 | 2,022 |
When does return-conditioned supervised learning work for offline reinforcement learning?
| 13 |
neurips
| 0 | 0 |
2023-06-16 22:57:06.008000
|
https://github.com/davidbrandfonbrener/rcsl-paper
| 6 |
When does return-conditioned supervised learning work for offline reinforcement learning?
|
https://scholar.google.com/scholar?cluster=13396358502953618671&hl=en&as_sdt=0,33
| 1 | 2,022 |
PDEBench: An Extensive Benchmark for Scientific Machine Learning
| 21 |
neurips
| 44 | 6 |
2023-06-16 22:57:06.220000
|
https://github.com/pdebench/pdebench
| 402 |
PDEBench: An extensive benchmark for scientific machine learning
|
https://scholar.google.com/scholar?cluster=15542719739478133736&hl=en&as_sdt=0,47
| 15 | 2,022 |
Learning Robust Dynamics through Variational Sparse Gating
| 0 |
neurips
| 1 | 1 |
2023-06-16 22:57:06.431000
|
https://github.com/arnavkj1995/vsg
| 19 |
Learning Robust Dynamics through Variational Sparse Gating
|
https://scholar.google.com/scholar?cluster=5582932369755688869&hl=en&as_sdt=0,36
| 2 | 2,022 |
Where to Pay Attention in Sparse Training for Feature Selection?
| 4 |
neurips
| 0 | 1 |
2023-06-16 22:57:06.641000
|
https://github.com/ghadasokar/wast
| 4 |
Where to Pay Attention in Sparse Training for Feature Selection?
|
https://scholar.google.com/scholar?cluster=1186481368031859899&hl=en&as_sdt=0,44
| 1 | 2,022 |
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
| 14 |
neurips
| 27 | 7 |
2023-06-16 22:57:06.853000
|
https://github.com/huanzhang12/alpha-beta-CROWN
| 148 |
General cutting planes for bound-propagation-based neural network verification
|
https://scholar.google.com/scholar?cluster=16952567700251161551&hl=en&as_sdt=0,44
| 8 | 2,022 |
Mildly Conservative Q-Learning for Offline Reinforcement Learning
| 15 |
neurips
| 3 | 0 |
2023-06-16 22:57:07.064000
|
https://github.com/dmksjfl/mcq
| 32 |
Mildly conservative Q-learning for offline reinforcement learning
|
https://scholar.google.com/scholar?cluster=11648694472509786601&hl=en&as_sdt=0,36
| 4 | 2,022 |
Functional Ensemble Distillation
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:57:07.275000
|
https://github.com/cobypenso/functional_ensemble_distillation
| 3 |
Functional Ensemble Distillation
|
https://scholar.google.com/scholar?cluster=7557864995422109600&hl=en&as_sdt=0,5
| 1 | 2,022 |
Lethal Dose Conjecture on Data Poisoning
| 3 |
neurips
| 0 | 0 |
2023-06-16 22:57:07.486000
|
https://github.com/wangwenxiao/FiniteAggregation
| 5 |
Lethal dose conjecture on data poisoning
|
https://scholar.google.com/scholar?cluster=10656232532262319468&hl=en&as_sdt=0,5
| 1 | 2,022 |
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
| 3 |
neurips
| 3 | 0 |
2023-06-16 22:57:07.697000
|
https://github.com/klimzaporojets/tempel
| 3 |
TempEL: Linking dynamically evolving and newly emerging entities
|
https://scholar.google.com/scholar?cluster=3241654383736118484&hl=en&as_sdt=0,5
| 1 | 2,022 |
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
| 2 |
neurips
| 2 | 0 |
2023-06-16 22:57:07.908000
|
https://github.com/weitianxin/HyperGCL
| 28 |
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
|
https://scholar.google.com/scholar?cluster=8987357747154997241&hl=en&as_sdt=0,5
| 2 | 2,022 |
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
| 6 |
neurips
| 3 | 1 |
2023-06-16 22:57:08.119000
|
https://github.com/alstn12088/sym-nco
| 13 |
Sym-nco: Leveraging symmetricity for neural combinatorial optimization
|
https://scholar.google.com/scholar?cluster=8234123365488999500&hl=en&as_sdt=0,33
| 1 | 2,022 |
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
| 93 |
neurips
| 39 | 9 |
2023-06-16 22:57:08.330000
|
https://github.com/r-three/t-few
| 298 |
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
|
https://scholar.google.com/scholar?cluster=242306292951569763&hl=en&as_sdt=0,1
| 6 | 2,022 |
DeepInteraction: 3D Object Detection via Modality Interaction
| 17 |
neurips
| 10 | 13 |
2023-06-16 22:57:08.540000
|
https://github.com/fudan-zvg/deepinteraction
| 162 |
Deepinteraction: 3d object detection via modality interaction
|
https://scholar.google.com/scholar?cluster=2369292758377733249&hl=en&as_sdt=0,15
| 19 | 2,022 |
Deep Differentiable Logic Gate Networks
| 2 |
neurips
| 20 | 0 |
2023-06-16 22:57:08.752000
|
https://github.com/felix-petersen/difflogic
| 241 |
Deep Differentiable Logic Gate Networks
|
https://scholar.google.com/scholar?cluster=12936836443171799268&hl=en&as_sdt=0,41
| 12 | 2,022 |
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:57:08.962000
|
https://github.com/parththaker/Bandits-GRUB
| 0 |
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
|
https://scholar.google.com/scholar?cluster=5836306663005448433&hl=en&as_sdt=0,5
| 4 | 2,022 |
GOOD: A Graph Out-of-Distribution Benchmark
| 14 |
neurips
| 15 | 1 |
2023-06-16 22:57:09.173000
|
https://github.com/divelab/good
| 130 |
Good: A graph out-of-distribution benchmark
|
https://scholar.google.com/scholar?cluster=5688487541372761713&hl=en&as_sdt=0,5
| 4 | 2,022 |
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:09.384000
|
https://github.com/jajajang/sparse
| 0 |
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
|
https://scholar.google.com/scholar?cluster=15122938357126562545&hl=en&as_sdt=0,5
| 1 | 2,022 |
What You See is What You Get: Principled Deep Learning via Distributional Generalization
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:57:09.594000
|
https://github.com/yangarbiter/dp-dg
| 6 |
What You See is What You Get: Principled Deep Learning via Distributional Generalization
|
https://scholar.google.com/scholar?cluster=2822362220882233132&hl=en&as_sdt=0,24
| 3 | 2,022 |
GAPX: Generalized Autoregressive Paraphrase-Identification X
| 0 |
neurips
| 0 | 0 |
2023-06-16 22:57:09.806000
|
https://github.com/yifeizhou02/generalized_paraphrase_identification
| 2 |
GAPX: Generalized Autoregressive Paraphrase-Identification X
|
https://scholar.google.com/scholar?cluster=17804560355779547348&hl=en&as_sdt=0,26
| 1 | 2,022 |
Scalable Infomin Learning
| 1 |
neurips
| 1 | 0 |
2023-06-16 22:57:10.016000
|
https://github.com/cyz-ai/infomin
| 8 |
Scalable Infomin Learning
|
https://scholar.google.com/scholar?cluster=10543006398190520114&hl=en&as_sdt=0,5
| 1 | 2,022 |
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
| 6 |
neurips
| 4 | 0 |
2023-06-16 22:57:10.227000
|
https://github.com/snap-stanford/le_pde
| 11 |
Learning to accelerate partial differential equations via latent global evolution
|
https://scholar.google.com/scholar?cluster=11413037155228818629&hl=en&as_sdt=0,43
| 41 | 2,022 |
Not too little, not too much: a theoretical analysis of graph (over)smoothing
| 13 |
neurips
| 0 | 0 |
2023-06-16 22:57:10.438000
|
https://github.com/nkeriven/graphsmoothing
| 2 |
Not too little, not too much: a theoretical analysis of graph (over) smoothing
|
https://scholar.google.com/scholar?cluster=2063487353980385484&hl=en&as_sdt=0,10
| 1 | 2,022 |
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
| 1 |
neurips
| 0 | 0 |
2023-06-16 22:57:10.650000
|
https://github.com/nerdslab/EIT
| 6 |
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
|
https://scholar.google.com/scholar?cluster=7588522259705770791&hl=en&as_sdt=0,33
| 1 | 2,022 |
Riemannian Score-Based Generative Modelling
| 41 |
neurips
| 12 | 2 |
2023-06-16 22:57:10.861000
|
https://github.com/oxcsml/riemannian-score-sde
| 56 |
Riemannian score-based generative modeling
|
https://scholar.google.com/scholar?cluster=11808970878216966405&hl=en&as_sdt=0,5
| 7 | 2,022 |
Open-Ended Reinforcement Learning with Neural Reward Functions
| 2 |
neurips
| 0 | 0 |
2023-06-16 22:57:11.074000
|
https://github.com/amujika/open-ended-reinforcement-learning-with-neural-reward-functions
| 8 |
Open-ended reinforcement learning with neural reward functions
|
https://scholar.google.com/scholar?cluster=12071061069808672843&hl=en&as_sdt=0,5
| 1 | 2,022 |
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
| 7 |
neurips
| 2 | 3 |
2023-06-16 22:57:11.285000
|
https://github.com/casia-iva-lab/obj2seq
| 72 |
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
|
https://scholar.google.com/scholar?cluster=9616302849095650848&hl=en&as_sdt=0,5
| 3 | 2,022 |
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
| 42 |
neurips
| 47 | 2 |
2023-06-16 22:57:11.495000
|
https://github.com/lupantech/ScienceQA
| 337 |
Learn to explain: Multimodal reasoning via thought chains for science question answering
|
https://scholar.google.com/scholar?cluster=15090414004847508782&hl=en&as_sdt=0,44
| 7 | 2,022 |
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