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Supported Policy Optimization for Offline Reinforcement Learning | 8 | neurips | 0 | 0 | 2023-06-16 23:00:01.967000 | https://github.com/thuml/SPOT | 10 | Supported policy optimization for offline reinforcement learning | https://scholar.google.com/scholar?cluster=6270305527768915360&hl=en&as_sdt=0,32 | 5 | 2,022 |
DDXPlus: A New Dataset For Automatic Medical Diagnosis | 6 | neurips | 0 | 1 | 2023-06-16 23:00:02.179000 | https://github.com/bruzwen/ddxplus | 12 | Ddxplus: A new dataset for automatic medical diagnosis | https://scholar.google.com/scholar?cluster=3028229938614227838&hl=en&as_sdt=0,5 | 1 | 2,022 |
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise | 8 | neurips | 0 | 0 | 2023-06-16 23:00:02.393000 | https://github.com/busycalibrating/clipped-stochastic-methods | 0 | Clipped stochastic methods for variational inequalities with heavy-tailed noise | https://scholar.google.com/scholar?cluster=3888795941605104858&hl=en&as_sdt=0,29 | 1 | 2,022 |
A Unified Sequence Interface for Vision Tasks | 33 | neurips | 55 | 21 | 2023-06-16 23:00:02.606000 | https://github.com/google-research/pix2seq | 650 | A unified sequence interface for vision tasks | https://scholar.google.com/scholar?cluster=14680303082655356082&hl=en&as_sdt=0,5 | 17 | 2,022 |
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning | 1 | neurips | 0 | 0 | 2023-06-16 23:00:02.818000 | https://github.com/project-pragma/expected-frequency-matrices-neurips-2022 | 0 | Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning | https://scholar.google.com/scholar?cluster=11960031454206755033&hl=en&as_sdt=0,18 | 0 | 2,022 |
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models | 11 | neurips | 11 | 7 | 2023-06-16 23:00:03.032000 | https://github.com/leonnnop/gmmseg | 124 | GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models | https://scholar.google.com/scholar?cluster=10640577107442772357&hl=en&as_sdt=0,3 | 11 | 2,022 |
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries | 26 | neurips | 3 | 0 | 2023-06-16 23:00:03.255000 | https://github.com/beabevi/sun | 32 | Understanding and extending subgraph gnns by rethinking their symmetries | https://scholar.google.com/scholar?cluster=14966370671903147583&hl=en&as_sdt=0,33 | 3 | 2,022 |
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning | 2 | neurips | 15 | 0 | 2023-06-16 23:00:03.501000 | https://github.com/kaist-dmlab/mqnet | 22 | Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning | https://scholar.google.com/scholar?cluster=12545815856367613650&hl=en&as_sdt=0,38 | 2 | 2,022 |
Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention | 2 | neurips | 1 | 0 | 2023-06-16 23:00:03.714000 | https://github.com/monstersecond/dasbe | 3 | Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention | https://scholar.google.com/scholar?cluster=6935604356068693641&hl=en&as_sdt=0,47 | 1 | 2,022 |
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation | 1 | neurips | 0 | 0 | 2023-06-16 23:00:03.927000 | https://github.com/graph-com/co_proxydesign | 12 | Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation | https://scholar.google.com/scholar?cluster=16393681527735462837&hl=en&as_sdt=0,29 | 0 | 2,022 |
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization | 5 | neurips | 0 | 1 | 2023-06-16 23:00:04.139000 | https://github.com/activatedgeek/tight-pac-bayes | 10 | PAC-bayes compression bounds so tight that they can explain generalization | https://scholar.google.com/scholar?cluster=7786972960841977431&hl=en&as_sdt=0,5 | 6 | 2,022 |
BagFlip: A Certified Defense Against Data Poisoning | 2 | neurips | 0 | 0 | 2023-06-16 23:00:04.362000 | https://github.com/foreverzyh/defend_framework | 2 | BagFlip: A Certified Defense against Data Poisoning | https://scholar.google.com/scholar?cluster=12286341512846726817&hl=en&as_sdt=0,47 | 2 | 2,022 |
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization | 3 | neurips | 2 | 1 | 2023-06-16 23:00:04.574000 | https://github.com/rddy/mimi | 21 | First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization | https://scholar.google.com/scholar?cluster=8778426534420691089&hl=en&as_sdt=0,10 | 1 | 2,022 |
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding | 2 | neurips | 5 | 1 | 2023-06-16 23:00:04.787000 | https://github.com/novicestone/hyperminer | 11 | Hyperminer: Topic taxonomy mining with hyperbolic embedding | https://scholar.google.com/scholar?cluster=9819074122178900305&hl=en&as_sdt=0,5 | 1 | 2,022 |
Visual Concepts Tokenization | 2 | neurips | 0 | 1 | 2023-06-16 23:00:05 | https://github.com/thomasmry/vct | 15 | Visual Concepts Tokenization | https://scholar.google.com/scholar?cluster=8458085041466969555&hl=en&as_sdt=0,19 | 2 | 2,022 |
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression | 11 | neurips | 1 | 0 | 2023-06-16 23:00:05.213000 | https://github.com/liboyue/beer | 4 | BEER: Fast Rate for Decentralized Nonconvex Optimization with Communication Compression | https://scholar.google.com/scholar?cluster=7137425453983349577&hl=en&as_sdt=0,33 | 1 | 2,022 |
Autoregressive Search Engines: Generating Substrings as Document Identifiers | 32 | neurips | 21 | 4 | 2023-06-16 23:00:05.429000 | https://github.com/facebookresearch/seal | 242 | Autoregressive search engines: Generating substrings as document identifiers | https://scholar.google.com/scholar?cluster=8414649729617248348&hl=en&as_sdt=0,44 | 6 | 2,022 |
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space | 14 | neurips | 5 | 1 | 2023-06-16 23:00:05.642000 | https://github.com/constrainedsampler/polytopesamplermatlab | 10 | Sampling with riemannian hamiltonian monte carlo in a constrained space | https://scholar.google.com/scholar?cluster=18009004710620196060&hl=en&as_sdt=0,48 | 5 | 2,022 |
Fair Ranking with Noisy Protected Attributes | 1 | neurips | 0 | 0 | 2023-06-16 23:00:05.855000 | https://github.com/anaymehrotra/fairrankingwithnoisyattributes | 2 | Fair Ranking with Noisy Protected Attributes | https://scholar.google.com/scholar?cluster=7053102236966869756&hl=en&as_sdt=0,10 | 1 | 2,022 |
Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations | 1 | neurips | 0 | 0 | 2023-06-16 23:00:06.067000 | https://github.com/cdslabamotong/social_inverse | 1 | Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations | https://scholar.google.com/scholar?cluster=10879672753670618782&hl=en&as_sdt=0,5 | 1 | 2,022 |
Modeling Human Exploration Through Resource-Rational Reinforcement Learning | 6 | neurips | 0 | 0 | 2023-06-16 23:00:06.323000 | https://github.com/marcelbinz/resource-rational-reinforcement-learning | 3 | Modeling Human Exploration Through Resource-Rational Reinforcement Learning | https://scholar.google.com/scholar?cluster=16794822202235026210&hl=en&as_sdt=0,5 | 1 | 2,022 |
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data | 0 | neurips | 1 | 3 | 2023-06-16 23:00:06.581000 | https://github.com/ailsaf/diggan | 5 | DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data | https://scholar.google.com/scholar?cluster=1540818415084096338&hl=en&as_sdt=0,44 | 2 | 2,022 |
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images | 86 | neurips | 293 | 31 | 2023-06-16 23:00:06.793000 | https://github.com/nv-tlabs/GET3D | 3,574 | Get3d: A generative model of high quality 3d textured shapes learned from images | https://scholar.google.com/scholar?cluster=16330894889594665221&hl=en&as_sdt=0,31 | 145 | 2,022 |
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks | 10 | neurips | 0 | 0 | 2023-06-16 23:00:07.006000 | https://github.com/DavideBuffelli/SizeShiftReg | 9 | Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks | https://scholar.google.com/scholar?cluster=17580849325875477854&hl=en&as_sdt=0,33 | 2 | 2,022 |
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning | 6 | neurips | 2 | 0 | 2023-06-16 23:00:07.221000 | https://github.com/aimagelab/lider | 8 | On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning | https://scholar.google.com/scholar?cluster=17593160937952833613&hl=en&as_sdt=0,14 | 3 | 2,022 |
MAgNet: Mesh Agnostic Neural PDE Solver | 5 | neurips | 2 | 1 | 2023-06-16 23:00:07.435000 | https://github.com/jaggbow/magnet | 25 | MAgnet: Mesh agnostic neural PDE solver | https://scholar.google.com/scholar?cluster=4350112799912824064&hl=en&as_sdt=0,5 | 1 | 2,022 |
Learning to Compare Nodes in Branch and Bound with Graph Neural Networks | 1 | neurips | 3 | 2 | 2023-06-16 23:00:07.648000 | https://github.com/ds4dm/learn2comparenodes | 13 | Learning to Compare Nodes in Branch and Bound with Graph Neural Networks | https://scholar.google.com/scholar?cluster=2705976177527772812&hl=en&as_sdt=0,5 | 4 | 2,022 |
ATD: Augmenting CP Tensor Decomposition by Self Supervision | 2 | neurips | 2 | 0 | 2023-06-16 23:00:07.862000 | https://github.com/ycq091044/atd | 6 | ATD: Augmenting CP Tensor Decomposition by Self Supervision | https://scholar.google.com/scholar?cluster=606172582822954692&hl=en&as_sdt=0,11 | 2 | 2,022 |
Towards Learning Universal Hyperparameter Optimizers with Transformers | 8 | neurips | 6 | 4 | 2023-06-16 23:00:08.077000 | https://github.com/google-research/optformer | 62 | Towards learning universal hyperparameter optimizers with transformers | https://scholar.google.com/scholar?cluster=16320840565400406534&hl=en&as_sdt=0,3 | 4 | 2,022 |
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations | 5 | neurips | 3 | 1 | 2023-06-16 23:00:08.289000 | https://github.com/Graph-Machine-Learning-Group/spin | 20 | Learning to reconstruct missing data from spatiotemporal graphs with sparse observations | https://scholar.google.com/scholar?cluster=8334658282792961058&hl=en&as_sdt=0,5 | 5 | 2,022 |
Peripheral Vision Transformer | 9 | neurips | 1 | 2 | 2023-06-16 23:00:08.509000 | https://github.com/juhongm999/pervit | 30 | Peripheral vision transformer | https://scholar.google.com/scholar?cluster=13097315276803844133&hl=en&as_sdt=0,5 | 2 | 2,022 |
ADBench: Anomaly Detection Benchmark | 60 | neurips | 101 | 9 | 2023-06-16 23:00:08.721000 | https://github.com/minqi824/adbench | 579 | Adbench: Anomaly detection benchmark | https://scholar.google.com/scholar?cluster=4407607921916219597&hl=en&as_sdt=0,21 | 12 | 2,022 |
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks | 6 | neurips | 7 | 1 | 2023-06-16 23:00:08.933000 | https://github.com/ikarosy/gated-lif | 26 | GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks | https://scholar.google.com/scholar?cluster=2076403375085197634&hl=en&as_sdt=0,47 | 2 | 2,022 |
Robust Reinforcement Learning using Offline Data | 16 | neurips | 1 | 0 | 2023-06-16 23:00:09.146000 | https://github.com/zaiyan-x/RFQI | 12 | Robust reinforcement learning using offline data | https://scholar.google.com/scholar?cluster=1874625503427910762&hl=en&as_sdt=0,23 | 2 | 2,022 |
Provably expressive temporal graph networks | 6 | neurips | 2 | 1 | 2023-06-16 23:00:09.360000 | https://github.com/aaltopml/pint | 11 | Provably expressive temporal graph networks | https://scholar.google.com/scholar?cluster=16593401358862246597&hl=en&as_sdt=0,5 | 8 | 2,022 |
First is Better Than Last for Language Data Influence | 1 | neurips | 0 | 0 | 2023-06-16 23:00:09.573000 | https://github.com/chihkuanyeh/TracIn-WE | 3 | First is Better Than Last for Language Data Influence | https://scholar.google.com/scholar?cluster=13589739763293612430&hl=en&as_sdt=0,33 | 2 | 2,022 |
Deep Combinatorial Aggregation | 1 | neurips | 1 | 0 | 2023-06-16 23:00:09.785000 | https://github.com/tum-vision/dca | 4 | Deep Combinatorial Aggregation | https://scholar.google.com/scholar?cluster=11229599811485616676&hl=en&as_sdt=0,3 | 12 | 2,022 |
Model-based Lifelong Reinforcement Learning with Bayesian Exploration | 0 | neurips | 1 | 0 | 2023-06-16 23:00:09.997000 | https://github.com/minusadd/vblrl | 6 | Model-based Lifelong Reinforcement Learning with Bayesian Exploration | https://scholar.google.com/scholar?cluster=1429823804057000001&hl=en&as_sdt=0,43 | 1 | 2,022 |
Debiased Self-Training for Semi-Supervised Learning | 7 | neurips | 3 | 1 | 2023-06-16 23:00:10.209000 | https://github.com/thuml/debiased-self-training | 37 | Debiased Self-Training for Semi-Supervised Learning | https://scholar.google.com/scholar?cluster=1562024070888687879&hl=en&as_sdt=0,47 | 4 | 2,022 |
Weak-shot Semantic Segmentation via Dual Similarity Transfer | 0 | neurips | 1 | 1 | 2023-06-16 23:00:10.422000 | https://github.com/bcmi/simformer-weak-shot-semantic-segmentation | 39 | Weak-shot Semantic Segmentation via Dual Similarity Transfer | https://scholar.google.com/scholar?cluster=1110171901581878158&hl=en&as_sdt=0,4 | 8 | 2,022 |
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction | 3 | neurips | 3 | 6 | 2023-06-16 23:00:10.634000 | https://github.com/lbox-kr/lbox-open | 70 | A multi-task benchmark for korean legal language understanding and judgement prediction | https://scholar.google.com/scholar?cluster=6229647396436406608&hl=en&as_sdt=0,33 | 5 | 2,022 |
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization | 5 | neurips | 1 | 1 | 2023-06-16 23:00:10.846000 | https://github.com/umd-huang-lab/transfer-fairness | 1 | Transferring fairness under distribution shifts via fair consistency regularization | https://scholar.google.com/scholar?cluster=2045183985933877126&hl=en&as_sdt=0,3 | 2 | 2,022 |
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection | 27 | neurips | 51 | 14 | 2023-06-16 23:00:11.058000 | https://github.com/jingkang50/openood | 459 | OpenOOD: Benchmarking generalized out-of-distribution detection | https://scholar.google.com/scholar?cluster=1091474511097006762&hl=en&as_sdt=0,14 | 5 | 2,022 |
KSD Aggregated Goodness-of-fit Test | 6 | neurips | 0 | 0 | 2023-06-16 23:00:11.274000 | https://github.com/antoninschrab/ksdagg-paper | 6 | KSD aggregated goodness-of-fit test | https://scholar.google.com/scholar?cluster=495383308571140426&hl=en&as_sdt=0,34 | 2 | 2,022 |
Efficient Risk-Averse Reinforcement Learning | 6 | neurips | 2 | 0 | 2023-06-16 23:00:11.486000 | https://github.com/ido90/CeSoR | 10 | Efficient risk-averse reinforcement learning | https://scholar.google.com/scholar?cluster=13537611132511952539&hl=en&as_sdt=0,23 | 1 | 2,022 |
Benefits of Additive Noise in Composing Classes with Bounded Capacity | 2 | neurips | 0 | 0 | 2023-06-16 23:00:11.698000 | https://github.com/fathollahpour/composition_noise | 0 | Benefits of Additive Noise in Composing Classes with Bounded Capacity | https://scholar.google.com/scholar?cluster=7148521168238224557&hl=en&as_sdt=0,5 | 2 | 2,022 |
Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning | 1 | neurips | 0 | 1 | 2023-06-16 23:00:11.910000 | https://github.com/huangzy225/3d-gcl | 4 | Towards hard-pose virtual try-on via 3d-aware global correspondence learning | https://scholar.google.com/scholar?cluster=18342403964707797536&hl=en&as_sdt=0,34 | 1 | 2,022 |
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection | 5 | neurips | 2 | 0 | 2023-06-16 23:00:12.122000 | https://github.com/bit-ml/anoshift | 32 | AnoShift: A distribution shift benchmark for unsupervised anomaly detection | https://scholar.google.com/scholar?cluster=17123244700110721819&hl=en&as_sdt=0,14 | 8 | 2,022 |
Towards Better Evaluation for Dynamic Link Prediction | 5 | neurips | 5 | 1 | 2023-06-16 23:00:12.333000 | https://github.com/fpour/dgb | 37 | Towards Better Evaluation for Dynamic Link Prediction | https://scholar.google.com/scholar?cluster=2464517726378679836&hl=en&as_sdt=0,5 | 1 | 2,022 |
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone | 27 | neurips | 7 | 6 | 2023-06-16 23:00:12.545000 | https://github.com/microsoft/fiber | 86 | Coarse-to-fine vision-language pre-training with fusion in the backbone | https://scholar.google.com/scholar?cluster=7539527092820284785&hl=en&as_sdt=0,5 | 8 | 2,022 |
VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning | 13 | neurips | 68 | 11 | 2023-06-16 23:00:12.757000 | https://github.com/facebookresearch/drqv2 | 269 | Vrl3: A data-driven framework for visual deep reinforcement learning | https://scholar.google.com/scholar?cluster=18285263434804961573&hl=en&as_sdt=0,5 | 9 | 2,022 |
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound | 0 | neurips | 1 | 0 | 2023-06-16 23:00:12.969000 | https://github.com/agup/soundness_saliency | 1 | New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound | https://scholar.google.com/scholar?cluster=10451995006190134842&hl=en&as_sdt=0,47 | 1 | 2,022 |
Finding and Listing Front-door Adjustment Sets | 2 | neurips | 0 | 0 | 2023-06-16 23:00:13.182000 | https://github.com/causalailab/frontdooradjustmentsets | 2 | Finding and Listing Front-door Adjustment Sets | https://scholar.google.com/scholar?cluster=5503246239780227329&hl=en&as_sdt=0,5 | 0 | 2,022 |
MorphTE: Injecting Morphology in Tensorized Embeddings | 2 | neurips | 3 | 0 | 2023-06-16 23:00:13.394000 | https://github.com/bigganbing/Fairseq_MorphTE | 13 | MorphTE: Injecting Morphology in Tensorized Embeddings | https://scholar.google.com/scholar?cluster=11621395100232513695&hl=en&as_sdt=0,31 | 1 | 2,022 |
Block-Recurrent Transformers | 21 | neurips | 18 | 1 | 2023-06-16 23:00:13.607000 | https://github.com/google-research/meliad | 158 | Block-recurrent transformers | https://scholar.google.com/scholar?cluster=15684096473797838415&hl=en&as_sdt=0,5 | 7 | 2,022 |
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling | 20 | neurips | 29 | 15 | 2023-06-16 23:00:13.819000 | https://github.com/Pointcept/Pointcept | 346 | Point transformer v2: Grouped vector attention and partition-based pooling | https://scholar.google.com/scholar?cluster=2723001857482086032&hl=en&as_sdt=0,39 | 10 | 2,022 |
Neural Approximation of Graph Topological Features | 3 | neurips | 1 | 1 | 2023-06-16 23:00:14.031000 | https://github.com/pkuyzy/TLC-GNN | 8 | Neural Approximation of Graph Topological Features | https://scholar.google.com/scholar?cluster=13389512890302778008&hl=en&as_sdt=0,10 | 2 | 2,022 |
MOVE: Unsupervised Movable Object Segmentation and Detection | 2 | neurips | 2 | 2 | 2023-06-16 23:00:14.244000 | https://github.com/adambielski/move-seg | 17 | MOVE: Unsupervised Movable Object Segmentation and Detection | https://scholar.google.com/scholar?cluster=8173455362624893467&hl=en&as_sdt=0,31 | 2 | 2,022 |
FreGAN: Exploiting Frequency Components for Training GANs under Limited Data | 3 | neurips | 1 | 0 | 2023-06-16 23:00:14.455000 | https://github.com/kobeshegu/fregan_neurips2022 | 38 | FreGAN: Exploiting Frequency Components for Training GANs under Limited Data | https://scholar.google.com/scholar?cluster=1815918906998343465&hl=en&as_sdt=0,5 | 1 | 2,022 |
Collaborative Decision Making Using Action Suggestions | 1 | neurips | 0 | 0 | 2023-06-16 23:00:14.668000 | https://github.com/sisl/action_suggestions | 7 | Collaborative Decision Making Using Action Suggestions | https://scholar.google.com/scholar?cluster=10468143367870116616&hl=en&as_sdt=0,5 | 2 | 2,022 |
Universally Expressive Communication in Multi-Agent Reinforcement Learning | 2 | neurips | 0 | 0 | 2023-06-16 23:00:14.880000 | https://github.com/mmorris44/expressive-gdns | 1 | Universally Expressive Communication in Multi-Agent Reinforcement Learning | https://scholar.google.com/scholar?cluster=8093380292358835878&hl=en&as_sdt=0,5 | 1 | 2,022 |
Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization | 3 | neurips | 0 | 0 | 2023-06-16 23:00:15.093000 | https://github.com/bpauld/pfw | 0 | Fast stochastic composite minimization and an accelerated frank-wolfe algorithm under parallelization | https://scholar.google.com/scholar?cluster=14357658369907461850&hl=en&as_sdt=0,34 | 1 | 2,022 |
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning | 8 | neurips | 10 | 7 | 2023-06-16 23:00:15.305000 | https://github.com/fuying-wang/mgca | 52 | Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning | https://scholar.google.com/scholar?cluster=16722403537302150812&hl=en&as_sdt=0,5 | 2 | 2,022 |
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation | 2 | neurips | 1 | 2 | 2023-06-16 23:00:15.517000 | https://github.com/feedzai/bank-account-fraud | 31 | Turning the tables: Biased, imbalanced, dynamic tabular datasets for ml evaluation | https://scholar.google.com/scholar?cluster=5567088687742955249&hl=en&as_sdt=0,44 | 4 | 2,022 |
Optimizing Relevance Maps of Vision Transformers Improves Robustness | 11 | neurips | 9 | 1 | 2023-06-16 23:00:15.729000 | https://github.com/hila-chefer/robustvit | 117 | Optimizing relevance maps of vision transformers improves robustness | https://scholar.google.com/scholar?cluster=4540065452590589915&hl=en&as_sdt=0,5 | 3 | 2,022 |
Deep Ensembles Work, But Are They Necessary? | 17 | neurips | 1 | 0 | 2023-06-16 23:00:15.940000 | https://github.com/cellistigs/interp_ensembles | 5 | Deep ensembles work, but are they necessary? | https://scholar.google.com/scholar?cluster=17084457719894473759&hl=en&as_sdt=0,34 | 1 | 2,022 |
VICE: Variational Interpretable Concept Embeddings | 5 | neurips | 3 | 0 | 2023-06-16 23:00:16.153000 | https://github.com/lukasmut/vice | 9 | VICE: Variational Interpretable Concept Embeddings | https://scholar.google.com/scholar?cluster=12914224895734200193&hl=en&as_sdt=0,44 | 6 | 2,022 |
Knowledge Distillation from A Stronger Teacher | 20 | neurips | 8 | 3 | 2023-06-16 23:00:16.365000 | https://github.com/hunto/dist_kd | 83 | Knowledge distillation from a stronger teacher | https://scholar.google.com/scholar?cluster=9782451594224614440&hl=en&as_sdt=0,6 | 2 | 2,022 |
Optimal Transport of Classifiers to Fairness | 1 | neurips | 1 | 0 | 2023-06-16 23:00:16.577000 | https://github.com/aida-ugent/OTF | 1 | Optimal Transport of Classifiers to Fairness | https://scholar.google.com/scholar?cluster=16219423422077161743&hl=en&as_sdt=0,5 | 4 | 2,022 |
Rethinking the compositionality of point clouds through regularization in the hyperbolic space | 6 | neurips | 2 | 2 | 2023-06-16 23:00:16.789000 | https://github.com/diegovalsesia/hycore | 15 | Rethinking the compositionality of point clouds through regularization in the hyperbolic space | https://scholar.google.com/scholar?cluster=5390155762200714510&hl=en&as_sdt=0,22 | 3 | 2,022 |
ReCo: Retrieve and Co-segment for Zero-shot Transfer | 18 | neurips | 5 | 5 | 2023-06-16 23:00:17.001000 | https://github.com/NoelShin/reco | 52 | Reco: Retrieve and co-segment for zero-shot transfer | https://scholar.google.com/scholar?cluster=2541893392537318474&hl=en&as_sdt=0,33 | 1 | 2,022 |
Monocular Dynamic View Synthesis: A Reality Check | 20 | neurips | 7 | 1 | 2023-06-16 23:00:17.214000 | https://github.com/kair-bair/dycheck | 130 | Monocular dynamic view synthesis: A reality check | https://scholar.google.com/scholar?cluster=4051245421926210617&hl=en&as_sdt=0,3 | 10 | 2,022 |
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection | 30 | neurips | 17 | 3 | 2023-06-16 23:00:17.426000 | https://github.com/hanoonaR/object-centric-ovd | 250 | Bridging the gap between object and image-level representations for open-vocabulary detection | https://scholar.google.com/scholar?cluster=2701303524777814353&hl=en&as_sdt=0,5 | 5 | 2,022 |
VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids | 48 | neurips | 6 | 0 | 2023-06-16 23:00:17.638000 | https://github.com/autonomousvision/voxgraf | 112 | Voxgraf: Fast 3d-aware image synthesis with sparse voxel grids | https://scholar.google.com/scholar?cluster=14022665138113076252&hl=en&as_sdt=0,5 | 17 | 2,022 |
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective | 9 | neurips | 0 | 0 | 2023-06-16 23:00:17.851000 | https://github.com/graph-com/bayesian_inference_based_gnn | 3 | Understanding non-linearity in graph neural networks from the bayesian-inference perspective | https://scholar.google.com/scholar?cluster=15550644623606214670&hl=en&as_sdt=0,5 | 0 | 2,022 |
Explainable Reinforcement Learning via Model Transforms | 1 | neurips | 0 | 1 | 2023-06-16 23:00:18.063000 | https://github.com/sarah-keren/rlpe | 1 | Explainable Reinforcement Learning via Model Transforms | https://scholar.google.com/scholar?cluster=12642694616127148920&hl=en&as_sdt=0,33 | 1 | 2,022 |
Self-Supervised Learning via Maximum Entropy Coding | 5 | neurips | 0 | 2 | 2023-06-16 23:00:18.278000 | https://github.com/xinliu20/mec | 33 | Self-supervised learning via maximum entropy coding | https://scholar.google.com/scholar?cluster=4670554254496466202&hl=en&as_sdt=0,14 | 12 | 2,022 |
A Practical, Progressively-Expressive GNN | 6 | neurips | 1 | 0 | 2023-06-16 23:00:18.492000 | https://github.com/lingxiaoshawn/kcsetgnn | 8 | A practical, progressively-expressive GNN | https://scholar.google.com/scholar?cluster=1801555861503089995&hl=en&as_sdt=0,5 | 1 | 2,022 |
On Learning Fairness and Accuracy on Multiple Subgroups | 8 | neurips | 1 | 0 | 2023-06-16 23:00:18.704000 | https://github.com/xugezheng/fams | 2 | On learning fairness and accuracy on multiple subgroups | https://scholar.google.com/scholar?cluster=4933287508687209050&hl=en&as_sdt=0,47 | 3 | 2,022 |
Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment | 2 | neurips | 5 | 12 | 2023-06-16 23:00:18.917000 | https://github.com/mediatechnologycenter/aestheval | 56 | Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment | https://scholar.google.com/scholar?cluster=15317983619506277329&hl=en&as_sdt=0,14 | 4 | 2,022 |
Black-box coreset variational inference | 1 | neurips | 4 | 0 | 2023-06-16 23:00:19.130000 | https://github.com/facebookresearch/blackbox-coresets-vi | 7 | Black-box Coreset Variational Inference | https://scholar.google.com/scholar?cluster=16155121271564700916&hl=en&as_sdt=0,44 | 8 | 2,022 |
Distilling Representations from GAN Generator via Squeeze and Span | 0 | neurips | 0 | 1 | 2023-06-16 23:00:19.341000 | https://github.com/yangyu12/squeeze-and-span | 7 | Distilling Representations from GAN Generator via Squeeze and Span | https://scholar.google.com/scholar?cluster=806447244804186364&hl=en&as_sdt=0,5 | 4 | 2,022 |
A Quantitative Geometric Approach to Neural-Network Smoothness | 2 | neurips | 0 | 0 | 2023-06-16 23:00:19.553000 | https://github.com/z1w/geolip | 0 | A Quantitative Geometric Approach to Neural Network Smoothness | https://scholar.google.com/scholar?cluster=6789257021629578865&hl=en&as_sdt=0,48 | 1 | 2,022 |
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning | 2 | neurips | 4 | 1 | 2023-06-16 23:00:19.765000 | https://github.com/zhanglijun95/AutoMTL | 47 | Automtl: A programming framework for automating efficient multi-task learning | https://scholar.google.com/scholar?cluster=7878464515755964059&hl=en&as_sdt=0,31 | 2 | 2,022 |
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare | 3 | neurips | 0 | 0 | 2023-06-16 23:00:19.977000 | https://github.com/mld3/offlinerl_factoredactions | 4 | Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare | https://scholar.google.com/scholar?cluster=6410167541170420183&hl=en&as_sdt=0,5 | 3 | 2,022 |
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy | 9 | neurips | 2 | 0 | 2023-06-16 23:00:20.190000 | https://github.com/anguyen8/visual-correspondence-xai | 37 | Visual correspondence-based explanations improve AI robustness and human-AI team accuracy | https://scholar.google.com/scholar?cluster=9102002858022546042&hl=en&as_sdt=0,44 | 3 | 2,022 |
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice | 2 | neurips | 0 | 0 | 2023-06-16 23:00:20.402000 | https://github.com/marcus-nordstrom/optimal-solutions-to-accuracy-and-dice | 1 | On image segmentation with noisy labels: Characterization and volume properties of the optimal solutions to accuracy and dice | https://scholar.google.com/scholar?cluster=12620304652080944393&hl=en&as_sdt=0,37 | 1 | 2,022 |
Fairness Reprogramming | 6 | neurips | 3 | 1 | 2023-06-16 23:00:20.614000 | https://github.com/ucsb-nlp-chang/fairness-reprogramming | 9 | Fairness reprogramming | https://scholar.google.com/scholar?cluster=10104950810882497858&hl=en&as_sdt=0,4 | 3 | 2,022 |
WeightedSHAP: analyzing and improving Shapley based feature attributions | 5 | neurips | 15 | 0 | 2023-06-16 23:00:20.827000 | https://github.com/ykwon0407/weightedshap | 146 | WeightedSHAP: analyzing and improving Shapley based feature attributions | https://scholar.google.com/scholar?cluster=15930531007434220976&hl=en&as_sdt=0,47 | 2 | 2,022 |
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks | 13 | neurips | 6 | 1 | 2023-06-16 23:00:21.040000 | https://github.com/princetonvisualai/rememberthepast-datasetdistillation | 27 | Remember the past: Distilling datasets into addressable memories for neural networks | https://scholar.google.com/scholar?cluster=10137780628795331558&hl=en&as_sdt=0,5 | 8 | 2,022 |
PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories | 4 | neurips | 1 | 2 | 2023-06-16 23:00:21.258000 | https://github.com/yuchenrao/PatchComplete | 31 | Patchcomplete: Learning multi-resolution patch priors for 3d shape completion on unseen categories | https://scholar.google.com/scholar?cluster=3702047949220397378&hl=en&as_sdt=0,1 | 2 | 2,022 |
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer | 15 | neurips | 7 | 11 | 2023-06-16 23:00:21.521000 | https://github.com/yanjingli0202/q-vit | 36 | Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer | https://scholar.google.com/scholar?cluster=3955595566743652517&hl=en&as_sdt=0,21 | 4 | 2,022 |
Local Latent Space Bayesian Optimization over Structured Inputs | 17 | neurips | 3 | 0 | 2023-06-16 23:00:21.733000 | https://github.com/nataliemaus/lolbo | 17 | Local latent space bayesian optimization over structured inputs | https://scholar.google.com/scholar?cluster=11212834051035239107&hl=en&as_sdt=0,5 | 3 | 2,022 |
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification | 4 | neurips | 0 | 1 | 2023-06-16 23:00:21.946000 | https://github.com/stephanieschoch/cs-shapley | 6 | CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification | https://scholar.google.com/scholar?cluster=1153833541425284379&hl=en&as_sdt=0,5 | 1 | 2,022 |
Factuality Enhanced Language Models for Open-Ended Text Generation | 14 | neurips | 0 | 0 | 2023-06-16 23:00:22.158000 | https://github.com/nayeon7lee/factualityprompt | 32 | Factuality enhanced language models for open-ended text generation | https://scholar.google.com/scholar?cluster=1383756650317492432&hl=en&as_sdt=0,5 | 3 | 2,022 |
On the Representation Collapse of Sparse Mixture of Experts | 13 | neurips | 1,868 | 365 | 2023-06-16 23:00:22.371000 | https://github.com/microsoft/unilm | 12,785 | On the representation collapse of sparse mixture of experts | https://scholar.google.com/scholar?cluster=3896458754067259677&hl=en&as_sdt=0,33 | 260 | 2,022 |
Towards Understanding Grokking: An Effective Theory of Representation Learning | 14 | neurips | 1 | 0 | 2023-06-16 23:00:22.583000 | https://github.com/ejmichaud/grokking-squared | 5 | Towards understanding grokking: An effective theory of representation learning | https://scholar.google.com/scholar?cluster=13179441772130531947&hl=en&as_sdt=0,10 | 3 | 2,022 |
Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference | 0 | neurips | 0 | 0 | 2023-06-16 23:00:22.796000 | https://github.com/segolenemartin/paddle | 3 | Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference | https://scholar.google.com/scholar?cluster=18347644409364092901&hl=en&as_sdt=0,5 | 1 | 2,022 |
Learning Manifold Dimensions with Conditional Variational Autoencoders | 2 | neurips | 0 | 0 | 2023-06-16 23:00:23.008000 | https://github.com/zhengyjzoe/manifold-dimensions-cvae | 3 | Learning Manifold Dimensions with Conditional Variational Autoencoders | https://scholar.google.com/scholar?cluster=10642265351504424278&hl=en&as_sdt=0,5 | 1 | 2,022 |
Optimal-er Auctions through Attention | 8 | neurips | 0 | 0 | 2023-06-16 23:00:23.220000 | https://github.com/dimonenka/optimaler | 6 | Optimal-er auctions through attention | https://scholar.google.com/scholar?cluster=16264768269300742707&hl=en&as_sdt=0,21 | 1 | 2,022 |
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