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Domain Generalization by Learning and Removing Domain-specific Features | 1 | neurips | 0 | 0 | 2023-06-16 22:59:19.310000 | https://github.com/yulearningg/LRDG | 8 | Domain Generalization by Learning and Removing Domain-specific Features | https://scholar.google.com/scholar?cluster=5494103796376605602&hl=en&as_sdt=0,10 | 1 | 2,022 |
Torsional Diffusion for Molecular Conformer Generation | 64 | neurips | 31 | 6 | 2023-06-16 22:59:19.524000 | https://github.com/gcorso/torsional-diffusion | 164 | Torsional diffusion for molecular conformer generation | https://scholar.google.com/scholar?cluster=1524640103154353919&hl=en&as_sdt=0,5 | 3 | 2,022 |
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators | 3 | neurips | 0 | 0 | 2023-06-16 22:59:19.735000 | https://github.com/wenkaixl/agrasst | 0 | AgraSSt: Approximate graph Stein statistics for interpretable assessment of implicit graph generators | https://scholar.google.com/scholar?cluster=8628149654729531365&hl=en&as_sdt=0,39 | 1 | 2,022 |
On the Limitations of Stochastic Pre-processing Defenses | 5 | neurips | 1 | 0 | 2023-06-16 22:59:19.947000 | https://github.com/wi-pi/stochastic-preprocessing-defenses | 0 | On the Limitations of Stochastic Pre-processing Defenses | https://scholar.google.com/scholar?cluster=7806519586437026308&hl=en&as_sdt=0,33 | 1 | 2,022 |
Proximal Point Imitation Learning | 3 | neurips | 1 | 0 | 2023-06-16 22:59:20.159000 | https://github.com/lviano/p2il | 3 | Proximal Point Imitation Learning | https://scholar.google.com/scholar?cluster=17949003719943015717&hl=en&as_sdt=0,5 | 1 | 2,022 |
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation | 1 | neurips | 1 | 0 | 2023-06-16 22:59:20.371000 | https://github.com/zkzhang98/microseg | 8 | Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation | https://scholar.google.com/scholar?cluster=10459431178117202282&hl=en&as_sdt=0,3 | 1 | 2,022 |
Smoothed Embeddings for Certified Few-Shot Learning | 0 | neurips | 0 | 0 | 2023-06-16 22:59:20.582000 | https://github.com/koava36/certrob-fewshot | 2 | Smoothed Embeddings for Certified Few-Shot Learning | https://scholar.google.com/scholar?cluster=5547919878197628339&hl=en&as_sdt=0,31 | 0 | 2,022 |
Group Meritocratic Fairness in Linear Contextual Bandits | 0 | neurips | 0 | 0 | 2023-06-16 22:59:20.793000 | https://github.com/csml-iit-ucl/gmfbandits | 1 | Group Meritocratic Fairness in Linear Contextual Bandits | https://scholar.google.com/scholar?cluster=9571107907427385262&hl=en&as_sdt=0,5 | 4 | 2,022 |
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm | 4 | neurips | 1 | 0 | 2023-06-16 22:59:21.005000 | https://github.com/akjayant/mbppol | 12 | Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm | https://scholar.google.com/scholar?cluster=7177631673389924386&hl=en&as_sdt=0,5 | 2 | 2,022 |
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | 1 | neurips | 0 | 0 | 2023-06-16 22:59:21.221000 | https://github.com/vothanhvinh/causalrff | 0 | An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | https://scholar.google.com/scholar?cluster=17335848302268873481&hl=en&as_sdt=0,25 | 2 | 2,022 |
Towards Improving Faithfulness in Abstractive Summarization | 4 | neurips | 0 | 0 | 2023-06-16 22:59:21.452000 | https://github.com/iriscxy/fes | 9 | Towards Improving Faithfulness in Abstractive Summarization | https://scholar.google.com/scholar?cluster=9202173853245340528&hl=en&as_sdt=0,5 | 2 | 2,022 |
ZIN: When and How to Learn Invariance Without Environment Partition? | 7 | neurips | 2 | 1 | 2023-06-16 22:59:21.663000 | https://github.com/linyongver/zin_official | 12 | ZIN: When and How to Learn Invariance Without Environment Partition? | https://scholar.google.com/scholar?cluster=16781280623432832625&hl=en&as_sdt=0,5 | 5 | 2,022 |
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation | 4 | neurips | 2 | 0 | 2023-06-16 22:59:21.875000 | https://github.com/jlko/active-surrogate-estimators | 5 | Active surrogate estimators: An active learning approach to label-efficient model evaluation | https://scholar.google.com/scholar?cluster=12181705407954202218&hl=en&as_sdt=0,36 | 1 | 2,022 |
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions | 1 | neurips | 2 | 2 | 2023-06-16 22:59:22.087000 | https://github.com/lchen001/hapi | 16 | HAPI: A large-scale longitudinal dataset of commercial ML API predictions | https://scholar.google.com/scholar?cluster=5762229029469931969&hl=en&as_sdt=0,5 | 2 | 2,022 |
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos | 69 | neurips | 105 | 6 | 2023-06-16 22:59:22.299000 | https://github.com/openai/Video-Pre-Training | 944 | Video pretraining (vpt): Learning to act by watching unlabeled online videos | https://scholar.google.com/scholar?cluster=17704984102832894583&hl=en&as_sdt=0,43 | 27 | 2,022 |
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization | 1 | neurips | 21 | 2 | 2023-06-16 22:59:22.512000 | https://github.com/uw-exp/globem | 110 | GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization | https://scholar.google.com/scholar?cluster=8900774154166669565&hl=en&as_sdt=0,15 | 11 | 2,022 |
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost | 0 | neurips | 0 | 0 | 2023-06-16 22:59:22.724000 | https://github.com/sc782/sbm-transformer | 10 | Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost | https://scholar.google.com/scholar?cluster=8950920198279158483&hl=en&as_sdt=0,41 | 1 | 2,022 |
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning | 17 | neurips | 10 | 3 | 2023-06-16 22:59:22.935000 | https://github.com/polixir/NeoRL | 76 | NeoRL: A near real-world benchmark for offline reinforcement learning | https://scholar.google.com/scholar?cluster=4124559435421105174&hl=en&as_sdt=0,34 | 4 | 2,022 |
Counterfactual Temporal Point Processes | 7 | neurips | 3 | 0 | 2023-06-16 22:59:23.146000 | https://github.com/networks-learning/counterfactual-ttp | 11 | Counterfactual temporal point processes | https://scholar.google.com/scholar?cluster=5471926667923328181&hl=en&as_sdt=0,14 | 3 | 2,022 |
Dungeons and Data: A Large-Scale NetHack Dataset | 1 | neurips | 102 | 16 | 2023-06-16 22:59:23.358000 | https://github.com/facebookresearch/nle | 871 | Dungeons and Data: A Large-Scale NetHack Dataset | https://scholar.google.com/scholar?cluster=10376659435054658161&hl=en&as_sdt=0,5 | 29 | 2,022 |
GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions | 7 | neurips | 10 | 0 | 2023-06-16 22:59:23.569000 | https://github.com/princeton-computational-imaging/gensdf | 87 | GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions | https://scholar.google.com/scholar?cluster=11531522694580627214&hl=en&as_sdt=0,41 | 7 | 2,022 |
Forecasting Human Trajectory from Scene History | 3 | neurips | 3 | 3 | 2023-06-16 22:59:23.780000 | https://github.com/makaruinah/shenet | 11 | Forecasting human trajectory from scene history | https://scholar.google.com/scholar?cluster=5059609174660170314&hl=en&as_sdt=0,47 | 1 | 2,022 |
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure | 7 | neurips | 11 | 0 | 2023-06-16 22:59:23.992000 | https://github.com/googlebaba/disc | 26 | Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure | https://scholar.google.com/scholar?cluster=8726960753760538986&hl=en&as_sdt=0,5 | 3 | 2,022 |
Asymptotics of $\ell_2$ Regularized Network Embeddings | 0 | neurips | 0 | 0 | 2023-06-16 22:59:24.204000 | https://github.com/aday651/embed-reg | 0 | Asymptotics of Regularized Network Embeddings | https://scholar.google.com/scholar?cluster=10375708066059724613&hl=en&as_sdt=0,5 | 1 | 2,022 |
On Embeddings for Numerical Features in Tabular Deep Learning | 19 | neurips | 18 | 1 | 2023-06-16 22:59:24.416000 | https://github.com/Yura52/tabular-dl-num-embeddings | 170 | On embeddings for numerical features in tabular deep learning | https://scholar.google.com/scholar?cluster=2553810460800723920&hl=en&as_sdt=0,1 | 4 | 2,022 |
Visual Prompting via Image Inpainting | 29 | neurips | 12 | 4 | 2023-06-16 22:59:24.628000 | https://github.com/amirbar/visual_prompting | 197 | Visual prompting via image inpainting | https://scholar.google.com/scholar?cluster=15899337886963537746&hl=en&as_sdt=0,5 | 12 | 2,022 |
OpenAUC: Towards AUC-Oriented Open-Set Recognition | 1 | neurips | 0 | 0 | 2023-06-16 22:59:24.839000 | https://github.com/wang22ti/openauc | 5 | OpenAUC: Towards AUC-Oriented Open-Set Recognition | https://scholar.google.com/scholar?cluster=17140867226806315612&hl=en&as_sdt=0,5 | 2 | 2,022 |
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT | 0 | neurips | 0 | 0 | 2023-06-16 22:59:25.051000 | https://github.com/cakcora/PersistentHomologyWithCoralPrunit | 3 | Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT | https://scholar.google.com/scholar?cluster=7224655115635333850&hl=en&as_sdt=0,10 | 2 | 2,022 |
GAUDI: A Neural Architect for Immersive 3D Scene Generation | 28 | neurips | 24 | 0 | 2023-06-16 22:59:25.264000 | https://github.com/apple/ml-gaudi | 577 | Gaudi: A neural architect for immersive 3d scene generation | https://scholar.google.com/scholar?cluster=14944404431434808615&hl=en&as_sdt=0,13 | 36 | 2,022 |
Mask-based Latent Reconstruction for Reinforcement Learning | 5 | neurips | 2 | 0 | 2023-06-16 22:59:25.476000 | https://github.com/microsoft/Mask-based-Latent-Reconstruction | 21 | Mask-based latent reconstruction for reinforcement learning | https://scholar.google.com/scholar?cluster=11030675521552103190&hl=en&as_sdt=0,5 | 5 | 2,022 |
Product Ranking for Revenue Maximization with Multiple Purchases | 1 | neurips | 0 | 0 | 2023-06-16 22:59:25.688000 | https://github.com/windxrz/mpb-ucb | 3 | Product Ranking for Revenue Maximization with Multiple Purchases | https://scholar.google.com/scholar?cluster=5497221065518652797&hl=en&as_sdt=0,33 | 1 | 2,022 |
One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations | 7 | neurips | 0 | 1 | 2023-06-16 22:59:25.899000 | https://github.com/kumapowerliu/ffclip | 36 | One model to edit them all: Free-form text-driven image manipulation with semantic modulations | https://scholar.google.com/scholar?cluster=9106501574546184017&hl=en&as_sdt=0,47 | 6 | 2,022 |
LieGG: Studying Learned Lie Group Generators | 5 | neurips | 0 | 0 | 2023-06-16 22:59:26.110000 | https://github.com/amoskalev/liegg | 3 | LieGG: Studying Learned Lie Group Generators | https://scholar.google.com/scholar?cluster=6458900076329173639&hl=en&as_sdt=0,5 | 1 | 2,022 |
FourierNets enable the design of highly non-local optical encoders for computational imaging | 2 | neurips | 2 | 0 | 2023-06-16 22:59:26.323000 | https://github.com/turagalab/snapshotscope | 3 | FourierNets enable the design of highly non-local optical encoders for computational imaging | https://scholar.google.com/scholar?cluster=17235458650551264923&hl=en&as_sdt=0,47 | 3 | 2,022 |
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks | 1 | neurips | 2 | 0 | 2023-06-16 22:59:26.534000 | https://github.com/dchiji-ntt/meta-ticket | 4 | Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks | https://scholar.google.com/scholar?cluster=355485473057301987&hl=en&as_sdt=0,5 | 1 | 2,022 |
LAION-5B: An open large-scale dataset for training next generation image-text models | 310 | neurips | 548 | 89 | 2023-06-16 22:59:26.746000 | https://github.com/mlfoundations/open_clip | 5,243 | Laion-5b: An open large-scale dataset for training next generation image-text models | https://scholar.google.com/scholar?cluster=8018158103125985189&hl=en&as_sdt=0,36 | 59 | 2,022 |
Constants of motion network | 2 | neurips | 0 | 0 | 2023-06-16 22:59:26.957000 | https://github.com/machine-discovery/comet | 2 | Constants of motion network | https://scholar.google.com/scholar?cluster=10578402621842665146&hl=en&as_sdt=0,33 | 3 | 2,022 |
Online Deep Equilibrium Learning for Regularization by Denoising | 6 | neurips | 8 | 2 | 2023-06-16 22:59:27.168000 | https://github.com/phernst/pytorch_radon | 22 | Online deep equilibrium learning for regularization by denoising | https://scholar.google.com/scholar?cluster=12374699513175757258&hl=en&as_sdt=0,6 | 2 | 2,022 |
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting | 12 | neurips | 41 | 2 | 2023-06-16 22:59:27.380000 | https://github.com/amazon-science/earth-forecasting-transformer | 212 | Earthformer: Exploring space-time transformers for earth system forecasting | https://scholar.google.com/scholar?cluster=6165560125598001271&hl=en&as_sdt=0,5 | 11 | 2,022 |
Benchopt: Reproducible, efficient and collaborative optimization benchmarks | 6 | neurips | 35 | 85 | 2023-06-16 22:59:27.591000 | https://github.com/benchopt/benchopt | 158 | Benchopt: Reproducible, efficient and collaborative optimization benchmarks | https://scholar.google.com/scholar?cluster=3504541958783431314&hl=en&as_sdt=0,33 | 6 | 2,022 |
SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems | 1 | neurips | 1 | 0 | 2023-06-16 22:59:27.803000 | https://github.com/sb-ai-lab/sketchboost-paper | 9 | SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems | https://scholar.google.com/scholar?cluster=12204750564848511287&hl=en&as_sdt=0,5 | 2 | 2,022 |
Decentralized Training of Foundation Models in Heterogeneous Environments | 10 | neurips | 10 | 3 | 2023-06-16 22:59:28.016000 | https://github.com/DS3Lab/DT-FM | 59 | Decentralized training of foundation models in heterogeneous environments | https://scholar.google.com/scholar?cluster=13763983237898796416&hl=en&as_sdt=0,3 | 1 | 2,022 |
Cross Aggregation Transformer for Image Restoration | 11 | neurips | 5 | 2 | 2023-06-16 22:59:28.230000 | https://github.com/zhengchen1999/cat | 77 | Cross Aggregation Transformer for Image Restoration | https://scholar.google.com/scholar?cluster=17495936545828523011&hl=en&as_sdt=0,43 | 3 | 2,022 |
DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems | 5 | neurips | 3 | 0 | 2023-06-16 22:59:28.452000 | https://github.com/dimesteam/dimes | 24 | DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems | https://scholar.google.com/scholar?cluster=7607751078671404883&hl=en&as_sdt=0,47 | 3 | 2,022 |
NSNet: A General Neural Probabilistic Framework for Satisfiability Problems | 0 | neurips | 3 | 0 | 2023-06-16 22:59:28.664000 | https://github.com/zhaoyu-li/nsnet | 13 | NSNet: A General Neural Probabilistic Framework for Satisfiability Problems | https://scholar.google.com/scholar?cluster=1383198639431989116&hl=en&as_sdt=0,5 | 1 | 2,022 |
Brain Network Transformer | 16 | neurips | 8 | 1 | 2023-06-16 22:59:28.876000 | https://github.com/wayfear/brainnetworktransformer | 34 | Brain network transformer | https://scholar.google.com/scholar?cluster=10818376030441199053&hl=en&as_sdt=0,31 | 2 | 2,022 |
Improved Utility Analysis of Private CountSketch | 4 | neurips | 0 | 0 | 2023-06-16 22:59:29.087000 | https://github.com/rasmus-pagh/private-countsketch | 3 | Improved Utility Analysis of Private CountSketch | https://scholar.google.com/scholar?cluster=9045975206203918002&hl=en&as_sdt=0,14 | 1 | 2,022 |
Improving Diffusion Models for Inverse Problems using Manifold Constraints | 53 | neurips | 15 | 1 | 2023-06-16 22:59:29.299000 | https://github.com/hj-harry/mcg_diffusion | 121 | Improving diffusion models for inverse problems using manifold constraints | https://scholar.google.com/scholar?cluster=18097862330271049483&hl=en&as_sdt=0,11 | 5 | 2,022 |
Deep Model Reassembly | 31 | neurips | 7 | 2 | 2023-06-16 22:59:29.511000 | https://github.com/adamdad/dery | 176 | Deep model reassembly | https://scholar.google.com/scholar?cluster=17041268371866200453&hl=en&as_sdt=0,43 | 2 | 2,022 |
BigBio: A Framework for Data-Centric Biomedical Natural Language Processing | 11 | neurips | 100 | 187 | 2023-06-16 22:59:29.723000 | https://github.com/bigscience-workshop/biomedical | 335 | Bigbio: a framework for data-centric biomedical natural language processing | https://scholar.google.com/scholar?cluster=16248185859280855738&hl=en&as_sdt=0,33 | 27 | 2,022 |
Gradient Estimation with Discrete Stein Operators | 6 | neurips | 0 | 0 | 2023-06-16 22:59:29.934000 | https://github.com/thjashin/rodeo | 15 | Gradient estimation with discrete Stein operators | https://scholar.google.com/scholar?cluster=17367160563592360698&hl=en&as_sdt=0,5 | 3 | 2,022 |
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems | 2 | neurips | 0 | 0 | 2023-06-16 22:59:30.145000 | https://github.com/changwoo-lee/rapidmtm | 3 | Rapidly mixing multiple-try Metropolis algorithms for model selection problems | https://scholar.google.com/scholar?cluster=8288484673444745873&hl=en&as_sdt=0,38 | 1 | 2,022 |
Online Agnostic Multiclass Boosting | 0 | neurips | 0 | 0 | 2023-06-16 22:59:30.356000 | https://github.com/vinodkraman/onlineagnosticmulticlassboosting | 0 | Online Agnostic Multiclass Boosting | https://scholar.google.com/scholar?cluster=17530449480068506498&hl=en&as_sdt=0,31 | 2 | 2,022 |
A contrastive rule for meta-learning | 14 | neurips | 0 | 1 | 2023-06-16 22:59:30.569000 | https://github.com/smonsays/contrastive-meta-learning | 9 | A contrastive rule for meta-learning | https://scholar.google.com/scholar?cluster=1536313672687965148&hl=en&as_sdt=0,33 | 1 | 2,022 |
Distinguishing Learning Rules with Brain Machine Interfaces | 2 | neurips | 0 | 0 | 2023-06-16 22:59:30.780000 | https://github.com/jacobfulano/learning-rules-with-bmi | 0 | Distinguishing learning rules with brain machine interfaces | https://scholar.google.com/scholar?cluster=11051656974979640667&hl=en&as_sdt=0,5 | 2 | 2,022 |
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex | 1 | neurips | 0 | 0 | 2023-06-16 22:59:30.991000 | https://github.com/jzf2101/alphatology | 3 | Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex | https://scholar.google.com/scholar?cluster=8603391882567020641&hl=en&as_sdt=0,5 | 1 | 2,022 |
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization | 7 | neurips | 0 | 3 | 2023-06-16 22:59:31.203000 | https://github.com/heimingx/semi_seg_proto | 22 | Semi-supervised semantic segmentation with prototype-based consistency regularization | https://scholar.google.com/scholar?cluster=2500907054917724227&hl=en&as_sdt=0,5 | 2 | 2,022 |
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms | 8 | neurips | 4 | 1 | 2023-06-16 22:59:31.414000 | https://github.com/smplbody/hmr-benchmarks | 106 | Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms | https://scholar.google.com/scholar?cluster=4376621748772936242&hl=en&as_sdt=0,24 | 8 | 2,022 |
TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning | 10 | neurips | 0 | 0 | 2023-06-16 22:59:31.626000 | https://github.com/andreichertkov/ttopt | 13 | TTOpt: A maximum volume quantized tensor train-based optimization and its application to reinforcement learning | https://scholar.google.com/scholar?cluster=6175341780524530089&hl=en&as_sdt=0,3 | 2 | 2,022 |
A Mixture Of Surprises for Unsupervised Reinforcement Learning | 1 | neurips | 0 | 1 | 2023-06-16 22:59:31.838000 | https://github.com/leaplabthu/moss | 13 | A Mixture of Surprises for Unsupervised Reinforcement Learning | https://scholar.google.com/scholar?cluster=9731296982002152035&hl=en&as_sdt=0,39 | 2 | 2,022 |
PeRFception: Perception using Radiance Fields | 2 | neurips | 15 | 9 | 2023-06-16 22:59:32.049000 | https://github.com/POSTECH-CVLab/PeRFception | 301 | PeRFception: Perception using Radiance Fields | https://scholar.google.com/scholar?cluster=13895322647029601648&hl=en&as_sdt=0,5 | 14 | 2,022 |
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems | 0 | neurips | 1 | 0 | 2023-06-16 22:59:32.261000 | https://github.com/ThyrixYang/gdfm_nips22 | 8 | Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems | https://scholar.google.com/scholar?cluster=10242536886571160185&hl=en&as_sdt=0,10 | 2 | 2,022 |
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks | 2 | neurips | 0 | 0 | 2023-06-16 22:59:32.472000 | https://github.com/mingruiliu-ml-lab/communication-efficient-local-gradient-clipping | 0 | A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks | https://scholar.google.com/scholar?cluster=5333604100052232790&hl=en&as_sdt=0,3 | 0 | 2,022 |
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models | 5 | neurips | 1 | 0 | 2023-06-16 22:59:32.684000 | https://github.com/kamildeja/analysing_ddgm | 5 | On analyzing generative and denoising capabilities of diffusion-based deep generative models | https://scholar.google.com/scholar?cluster=995225694240773141&hl=en&as_sdt=0,10 | 1 | 2,022 |
DiSC: Differential Spectral Clustering of Features | 0 | neurips | 1 | 0 | 2023-06-16 22:59:32.896000 | https://github.com/Mishne-Lab/DiSC | 2 | DiSC: Differential Spectral Clustering of Features | https://scholar.google.com/scholar?cluster=7617996408610291337&hl=en&as_sdt=0,33 | 2 | 2,022 |
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes | 24 | neurips | 60 | 6 | 2023-06-16 22:59:33.108000 | https://github.com/google-research/big_vision | 890 | Uvim: A unified modeling approach for vision with learned guiding codes | https://scholar.google.com/scholar?cluster=13016594180316687621&hl=en&as_sdt=0,5 | 23 | 2,022 |
Proximal Learning With Opponent-Learning Awareness | 1 | neurips | 3 | 4 | 2023-06-16 22:59:33.320000 | https://github.com/silent-zebra/pola | 4 | Proximal Learning With Opponent-Learning Awareness | https://scholar.google.com/scholar?cluster=6796004730417376000&hl=en&as_sdt=0,5 | 5 | 2,022 |
Coresets for Wasserstein Distributionally Robust Optimization Problems | 0 | neurips | 0 | 0 | 2023-06-16 22:59:33.532000 | https://github.com/h305142/wdro_coreset | 2 | Coresets for Wasserstein Distributionally Robust Optimization Problems | https://scholar.google.com/scholar?cluster=15328832581114921682&hl=en&as_sdt=0,5 | 2 | 2,022 |
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning | 5 | neurips | 2 | 8 | 2023-06-16 22:59:33.744000 | https://github.com/linziyi96/st-adapter | 20 | St-adapter: Parameter-efficient image-to-video transfer learning | https://scholar.google.com/scholar?cluster=16710270545076573950&hl=en&as_sdt=0,23 | 6 | 2,022 |
Can Adversarial Training Be Manipulated By Non-Robust Features? | 1 | neurips | 0 | 0 | 2023-06-16 22:59:33.955000 | https://github.com/tlmichael/hypocritical-perturbation | 2 | Can Adversarial Training Be Manipulated By Non-Robust Features? | https://scholar.google.com/scholar?cluster=7120256042443644794&hl=en&as_sdt=0,3 | 1 | 2,022 |
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning | 7 | neurips | 1 | 0 | 2023-06-16 22:59:34.166000 | https://github.com/gilgameshd/grader | 13 | Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning | https://scholar.google.com/scholar?cluster=3294634165353463281&hl=en&as_sdt=0,10 | 2 | 2,022 |
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models | 6 | neurips | 0 | 0 | 2023-06-16 22:59:34.378000 | https://github.com/winogavil/winogavil-experiments | 1 | WinoGAViL: Gamified association benchmark to challenge vision-and-language models | https://scholar.google.com/scholar?cluster=2502557314883549286&hl=en&as_sdt=0,26 | 1 | 2,022 |
Elucidating the Design Space of Diffusion-Based Generative Models | 182 | neurips | 52 | 4 | 2023-06-16 22:59:34.589000 | https://github.com/nvlabs/edm | 611 | Elucidating the design space of diffusion-based generative models | https://scholar.google.com/scholar?cluster=5258718823597512255&hl=en&as_sdt=0,5 | 28 | 2,022 |
Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent | 2 | neurips | 0 | 0 | 2023-06-16 22:59:34.802000 | https://github.com/shoelim/mpgd | 1 | Chaotic regularization and heavy-tailed limits for deterministic gradient descent | https://scholar.google.com/scholar?cluster=15394418026673969383&hl=en&as_sdt=0,48 | 2 | 2,022 |
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments | 0 | neurips | 2 | 1 | 2023-06-16 22:59:35.014000 | https://github.com/smpl-env/smpl | 12 | SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments | https://scholar.google.com/scholar?cluster=11656776523650390398&hl=en&as_sdt=0,33 | 3 | 2,022 |
The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models | 8 | neurips | 3,103 | 884 | 2023-06-16 22:59:35.235000 | https://github.com/microsoft/DeepSpeed | 25,950 | The stability-efficiency dilemma: Investigating sequence length warmup for training GPT models | https://scholar.google.com/scholar?cluster=2863317000596137587&hl=en&as_sdt=0,44 | 290 | 2,022 |
Generalization Gap in Amortized Inference | 5 | neurips | 0 | 0 | 2023-06-16 22:59:35.455000 | https://github.com/zmtomorrow/generalizationgapinamortizedinference | 1 | Generalization gap in amortized inference | https://scholar.google.com/scholar?cluster=8684926098848417995&hl=en&as_sdt=0,1 | 1 | 2,022 |
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation | 0 | neurips | 0 | 0 | 2023-06-16 22:59:35.667000 | https://github.com/rehg-lab/pulseimpute | 14 | PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation | https://scholar.google.com/scholar?cluster=16974721368633186321&hl=en&as_sdt=0,5 | 4 | 2,022 |
What are the best Systems? New Perspectives on NLP Benchmarking | 7 | neurips | 3 | 3 | 2023-06-16 22:59:35.879000 | https://github.com/pierrecolombo/rankingnlpsystems | 12 | What are the best systems? new perspectives on nlp benchmarking | https://scholar.google.com/scholar?cluster=6399800265216949784&hl=en&as_sdt=0,33 | 1 | 2,022 |
Learning from Label Proportions by Learning with Label Noise | 4 | neurips | 0 | 0 | 2023-06-16 22:59:36.091000 | https://github.com/z-jianxin/llpfc | 3 | Learning from label proportions by learning with label noise | https://scholar.google.com/scholar?cluster=5147088171143783724&hl=en&as_sdt=0,5 | 1 | 2,022 |
Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution | 5 | neurips | 0 | 0 | 2023-06-16 22:59:36.302000 | https://github.com/aorvieto/decsps | 4 | Dynamics of sgd with stochastic polyak stepsizes: Truly adaptive variants and convergence to exact solution | https://scholar.google.com/scholar?cluster=1202208377216276410&hl=en&as_sdt=0,25 | 1 | 2,022 |
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs | 15 | neurips | 92 | 3 | 2023-06-16 22:59:36.515000 | https://github.com/pygod-team/pygod | 906 | Bond: Benchmarking unsupervised outlier node detection on static attributed graphs | https://scholar.google.com/scholar?cluster=4649486946947801284&hl=en&as_sdt=0,10 | 11 | 2,022 |
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training | 45 | neurips | 17 | 5 | 2023-06-16 22:59:36.727000 | https://github.com/zrrskywalker/point-m2ae | 130 | Point-M2AE: multi-scale masked autoencoders for hierarchical point cloud pre-training | https://scholar.google.com/scholar?cluster=8230127879015912569&hl=en&as_sdt=0,21 | 11 | 2,022 |
Exploring Example Influence in Continual Learning | 8 | neurips | 0 | 0 | 2023-06-16 22:59:36.938000 | https://github.com/sssunqing/example_influence_cl | 14 | Exploring Example Influence in Continual Learning | https://scholar.google.com/scholar?cluster=4168097285182390934&hl=en&as_sdt=0,5 | 1 | 2,022 |
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap | 0 | neurips | 1 | 0 | 2023-06-16 22:59:37.150000 | https://github.com/lucpoisson/subspaceclustering | 1 | Subspace clustering in high-dimensions: Phase transitions\& Statistical-to-Computational gap | https://scholar.googleusercontent.com/scholar?q=cache:4HwC6_qwHhoJ:scholar.google.com/+Subspace+clustering+in+high-dimensions:+Phase+transitions+%26+Statistical-to-Computational+gap&hl=en&as_sdt=0,3 | 1 | 2,022 |
How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders | 4 | neurips | 4 | 0 | 2023-06-16 22:59:37.362000 | https://github.com/zhangq327/u-mae | 32 | How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders | https://scholar.google.com/scholar?cluster=12421230382199683849&hl=en&as_sdt=0,34 | 3 | 2,022 |
Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors | 1 | neurips | 0 | 0 | 2023-06-16 22:59:37.575000 | https://github.com/novaglow646/nips22-mat-and-ldat-for-ood | 6 | Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors | https://scholar.google.com/scholar?cluster=847890003773472313&hl=en&as_sdt=0,44 | 1 | 2,022 |
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers | 34 | neurips | 3,103 | 884 | 2023-06-16 22:59:37.787000 | https://github.com/microsoft/DeepSpeed | 25,950 | Zeroquant: Efficient and affordable post-training quantization for large-scale transformers | https://scholar.google.com/scholar?cluster=14601198018737164595&hl=en&as_sdt=0,33 | 290 | 2,022 |
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping | 0 | neurips | 0 | 0 | 2023-06-16 22:59:37.998000 | https://github.com/rrags/ProtoX_NeurIPS | 2 | ProtoX: Explaining a Reinforcement Learning Agent via Prototyping | https://scholar.google.com/scholar?cluster=15061235494194718383&hl=en&as_sdt=0,46 | 1 | 2,022 |
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation | 12 | neurips | 3 | 0 | 2023-06-16 22:59:38.210000 | https://github.com/taesikgong/note | 25 | Note: Robust continual test-time adaptation against temporal correlation | https://scholar.google.com/scholar?cluster=342119686943612993&hl=en&as_sdt=0,5 | 1 | 2,022 |
Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation | 6 | neurips | 1 | 2 | 2023-06-16 22:59:38.422000 | https://github.com/zoilsen/clom | 6 | Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation | https://scholar.google.com/scholar?cluster=13623703872858769843&hl=en&as_sdt=0,3 | 1 | 2,022 |
Forecasting Future World Events With Neural Networks | 7 | neurips | 50 | 1 | 2023-06-16 22:59:38.635000 | https://github.com/andyzoujm/autocast | 157 | Forecasting Future World Events with Neural Networks | https://scholar.google.com/scholar?cluster=17792483394679760594&hl=en&as_sdt=0,5 | 7 | 2,022 |
Autoregressive Perturbations for Data Poisoning | 9 | neurips | 3 | 0 | 2023-06-16 22:59:38.847000 | https://github.com/psandovalsegura/autoregressive-poisoning | 10 | Autoregressive perturbations for data poisoning | https://scholar.google.com/scholar?cluster=17109390722215919135&hl=en&as_sdt=0,47 | 3 | 2,022 |
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine | 1 | neurips | 1 | 0 | 2023-06-16 22:59:39.059000 | https://github.com/bilkent-cyborg/escada | 1 | Escada: Efficient safety and context aware dose allocation for precision medicine | https://scholar.google.com/scholar?cluster=12799291179330941239&hl=en&as_sdt=0,3 | 1 | 2,022 |
Improved Algorithms for Neural Active Learning | 2 | neurips | 0 | 0 | 2023-06-16 22:59:39.270000 | https://github.com/matouk98/i-neural | 0 | Improved Algorithms for Neural Active Learning | https://scholar.google.com/scholar?cluster=14846687732402339872&hl=en&as_sdt=0,33 | 1 | 2,022 |
CUP: Critic-Guided Policy Reuse | 1 | neurips | 1 | 0 | 2023-06-16 22:59:39.482000 | https://github.com/nagisazj/cup | 4 | CUP: Critic-Guided Policy Reuse | https://scholar.google.com/scholar?cluster=11253594017005639050&hl=en&as_sdt=0,39 | 1 | 2,022 |
QUARK: Controllable Text Generation with Reinforced Unlearning | 25 | neurips | 9 | 1 | 2023-06-16 22:59:39.693000 | https://github.com/gximinglu/quark | 50 | Quark: Controllable text generation with reinforced unlearning | https://scholar.google.com/scholar?cluster=15982538186848433892&hl=en&as_sdt=0,31 | 3 | 2,022 |
Parameter-free Dynamic Graph Embedding for Link Prediction | 2 | neurips | 1 | 1 | 2023-06-16 22:59:39.904000 | https://github.com/fudancisl/freegem | 9 | Parameter-free Dynamic Graph Embedding for Link Prediction | https://scholar.google.com/scholar?cluster=737985874382634688&hl=en&as_sdt=0,44 | 1 | 2,022 |
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning | 3 | neurips | 0 | 0 | 2023-06-16 22:59:40.115000 | https://github.com/jaearly/mil-for-non-markovian-reward-modelling | 3 | Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning | https://scholar.google.com/scholar?cluster=9372994966597211189&hl=en&as_sdt=0,47 | 1 | 2,022 |
Explaining Preferences with Shapley Values | 1 | neurips | 0 | 0 | 2023-06-16 22:59:40.327000 | https://github.com/mrhuff/pref-shap | 3 | Explaining Preferences with Shapley Values | https://scholar.google.com/scholar?cluster=13809288685377851579&hl=en&as_sdt=0,5 | 2 | 2,022 |
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