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Risk-averse Heteroscedastic Bayesian Optimization | 17 | neurips | 1 | 0 | 2023-06-16 16:07:13.244000 | https://github.com/avidereta/risk-averse-hetero-bo | 9 | Risk-averse heteroscedastic bayesian optimization | https://scholar.google.com/scholar?cluster=2848342155856072637&hl=en&as_sdt=0,10 | 3 | 2,021 |
Invertible DenseNets with Concatenated LipSwish | 11 | neurips | 3 | 0 | 2023-06-16 16:07:13.444000 | https://github.com/yperugachidiaz/invertible_densenets | 19 | Invertible densenets with concatenated lipswish | https://scholar.google.com/scholar?cluster=9347075628152014581&hl=en&as_sdt=0,10 | 1 | 2,021 |
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning | 10 | neurips | 1 | 0 | 2023-06-16 16:07:13.675000 | https://github.com/akshaymehra24/LimitsOfUDA | 6 | Understanding the limits of unsupervised domain adaptation via data poisoning | https://scholar.google.com/scholar?cluster=12714833644875201572&hl=en&as_sdt=0,36 | 3 | 2,021 |
Scatterbrain: Unifying Sparse and Low-rank Attention | 24 | neurips | 17 | 11 | 2023-06-16 16:07:13.876000 | https://github.com/hazyresearch/scatterbrain | 127 | Scatterbrain: Unifying sparse and low-rank attention | https://scholar.google.com/scholar?cluster=6782072706474039157&hl=en&as_sdt=0,5 | 22 | 2,021 |
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial | 10 | neurips | 0 | 1 | 2023-06-16 16:07:14.093000 | https://github.com/ucsc-real/canlessbemore | 2 | Can less be more? when increasing-to-balancing label noise rates considered beneficial | https://scholar.google.com/scholar?cluster=14641601547710945131&hl=en&as_sdt=0,14 | 2 | 2,021 |
Projected GANs Converge Faster | 90 | neurips | 94 | 19 | 2023-06-16 16:07:14.293000 | https://github.com/autonomousvision/projected_gan | 843 | Projected gans converge faster | https://scholar.google.com/scholar?cluster=3804763149823389605&hl=en&as_sdt=0,10 | 34 | 2,021 |
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains | 13 | neurips | 3 | 0 | 2023-06-16 16:07:14.493000 | https://github.com/martius-lab/gatel0rd | 18 | Sparsely changing latent states for prediction and planning in partially observable domains | https://scholar.google.com/scholar?cluster=9739662253385511838&hl=en&as_sdt=0,47 | 2 | 2,021 |
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning | 12 | neurips | 1 | 0 | 2023-06-16 16:07:14.693000 | https://github.com/neeharperi/PreferenceNet | 13 | Preferencenet: Encoding human preferences in auction design with deep learning | https://scholar.google.com/scholar?cluster=4524270007182675822&hl=en&as_sdt=0,5 | 4 | 2,021 |
Large-Scale Learning with Fourier Features and Tensor Decompositions | 2 | neurips | 0 | 0 | 2023-06-16 16:07:14.899000 | https://github.com/fwesel/t-krr | 5 | Large-Scale Learning with Fourier Features and Tensor Decompositions | https://scholar.google.com/scholar?cluster=12138145385303817860&hl=en&as_sdt=0,10 | 1 | 2,021 |
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks | 5 | neurips | 7 | 0 | 2023-06-16 16:07:15.105000 | https://github.com/IBM/sau-explore | 9 | Deep bandits show-off: Simple and efficient exploration with deep networks | https://scholar.google.com/scholar?cluster=401634264438416272&hl=en&as_sdt=0,5 | 5 | 2,021 |
Regret Minimization Experience Replay in Off-Policy Reinforcement Learning | 15 | neurips | 0 | 0 | 2023-06-16 16:07:15.305000 | https://github.com/aidefender/remern-remert | 6 | Regret minimization experience replay in off-policy reinforcement learning | https://scholar.google.com/scholar?cluster=7565091361055493573&hl=en&as_sdt=0,33 | 3 | 2,021 |
Relative Uncertainty Learning for Facial Expression Recognition | 54 | neurips | 5 | 2 | 2023-06-16 16:07:15.505000 | https://github.com/zyh-uaiaaaa/relative-uncertainty-learning | 41 | Relative uncertainty learning for facial expression recognition | https://scholar.google.com/scholar?cluster=16134891885738614873&hl=en&as_sdt=0,15 | 1 | 2,021 |
An Information-theoretic Approach to Distribution Shifts | 7 | neurips | 1 | 0 | 2023-06-16 16:07:15.706000 | https://github.com/mfederici/dsit | 21 | An information-theoretic approach to distribution shifts | https://scholar.google.com/scholar?cluster=7250154476412020577&hl=en&as_sdt=0,32 | 1 | 2,021 |
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors | 12 | neurips | 1 | 0 | 2023-06-16 16:07:15.908000 | https://github.com/liuzq09/PRI_SPCA | 1 | Towards sample-optimal compressive phase retrieval with sparse and generative priors | https://scholar.google.com/scholar?cluster=14915702811632236465&hl=en&as_sdt=0,14 | 2 | 2,021 |
Moser Flow: Divergence-based Generative Modeling on Manifolds | 21 | neurips | 3 | 1 | 2023-06-16 16:07:16.109000 | https://github.com/noamroze/moser_flow | 13 | Moser flow: Divergence-based generative modeling on manifolds | https://scholar.google.com/scholar?cluster=17329804077540165882&hl=en&as_sdt=0,4 | 1 | 2,021 |
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling | 117 | neurips | 9 | 0 | 2023-06-16 16:07:16.308000 | https://github.com/JTT94/diffusion_schrodinger_bridge | 72 | Diffusion Schrödinger bridge with applications to score-based generative modeling | https://scholar.google.com/scholar?cluster=318258828713196441&hl=en&as_sdt=0,5 | 2 | 2,021 |
Improving Transferability of Representations via Augmentation-Aware Self-Supervision | 16 | neurips | 6 | 3 | 2023-06-16 16:07:16.508000 | https://github.com/hankook/augself | 41 | Improving transferability of representations via augmentation-aware self-supervision | https://scholar.google.com/scholar?cluster=14853889863955201608&hl=en&as_sdt=0,5 | 3 | 2,021 |
Long-Short Transformer: Efficient Transformers for Language and Vision | 64 | neurips | 34 | 0 | 2023-06-16 16:07:16.709000 | https://github.com/NVIDIA/transformer-ls | 211 | Long-short transformer: Efficient transformers for language and vision | https://scholar.google.com/scholar?cluster=9337214669127097563&hl=en&as_sdt=0,11 | 15 | 2,021 |
Post-Training Sparsity-Aware Quantization | 19 | neurips | 3 | 0 | 2023-06-16 16:07:16.909000 | https://github.com/gilshm/sparq | 27 | Post-training sparsity-aware quantization | https://scholar.google.com/scholar?cluster=9389319073093341225&hl=en&as_sdt=0,14 | 2 | 2,021 |
Deconditional Downscaling with Gaussian Processes | 12 | neurips | 0 | 0 | 2023-06-16 16:07:17.109000 | https://github.com/shahineb/deconditional-downscaling | 0 | Deconditional downscaling with gaussian processes | https://scholar.google.com/scholar?cluster=16062448146360267282&hl=en&as_sdt=0,5 | 1 | 2,021 |
Per-Pixel Classification is Not All You Need for Semantic Segmentation | 480 | neurips | 139 | 9 | 2023-06-16 16:07:17.308000 | https://github.com/facebookresearch/MaskFormer | 1,137 | Per-pixel classification is not all you need for semantic segmentation | https://scholar.google.com/scholar?cluster=8508636578765152299&hl=en&as_sdt=0,38 | 22 | 2,021 |
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data | 5 | neurips | 0 | 0 | 2023-06-16 16:07:17.509000 | https://github.com/ostadabbas/deep-markov-factor-analysis-dmfa- | 2 | Deep markov factor analysis: Towards concurrent temporal and spatial analysis of fmri data | https://scholar.google.com/scholar?cluster=1299626113099194019&hl=en&as_sdt=0,5 | 1 | 2,021 |
BooVAE: Boosting Approach for Continual Learning of VAE | 9 | neurips | 0 | 0 | 2023-06-16 16:07:17.717000 | https://github.com/AKuzina/BooVAE | 9 | BooVAE: Boosting approach for continual learning of VAE | https://scholar.google.com/scholar?cluster=5333948931157317113&hl=en&as_sdt=0,5 | 3 | 2,021 |
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL | 6 | neurips | 0 | 0 | 2023-06-16 16:07:17.916000 | https://github.com/wange011/offline-pessimistic | 3 | Pessimism meets invariance: Provably efficient offline mean-field multi-agent RL | https://scholar.google.com/scholar?cluster=1776481233969285498&hl=en&as_sdt=0,5 | 2 | 2,021 |
Emergent Communication of Generalizations | 14 | neurips | 0 | 0 | 2023-06-16 16:07:18.116000 | https://github.com/jayelm/emergent-generalization | 11 | Emergent communication of generalizations | https://scholar.google.com/scholar?cluster=2371943873159023956&hl=en&as_sdt=0,39 | 2 | 2,021 |
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification | 25 | neurips | 12 | 2 | 2023-06-16 16:07:18.316000 | https://github.com/stadlmax/Graph-Posterior-Network | 32 | Graph posterior network: Bayesian predictive uncertainty for node classification | https://scholar.google.com/scholar?cluster=2435401315615096507&hl=en&as_sdt=0,5 | 4 | 2,021 |
No-Press Diplomacy from Scratch | 21 | neurips | 12 | 6 | 2023-06-16 16:07:18.516000 | https://github.com/facebookresearch/diplomacy_searchbot | 37 | No-press diplomacy from scratch | https://scholar.google.com/scholar?cluster=15217875294389573451&hl=en&as_sdt=0,21 | 11 | 2,021 |
Learning latent causal graphs via mixture oracles | 19 | neurips | 1 | 0 | 2023-06-16 16:07:18.716000 | https://github.com/30bohdan/latent-dag | 4 | Learning latent causal graphs via mixture oracles | https://scholar.google.com/scholar?cluster=1334743308550878246&hl=en&as_sdt=0,5 | 3 | 2,021 |
Deep Contextual Video Compression | 73 | neurips | 8 | 3 | 2023-06-16 16:07:18.919000 | https://github.com/DeepMC-DCVC/DCVC | 52 | Deep contextual video compression | https://scholar.google.com/scholar?cluster=7877485587962972033&hl=en&as_sdt=0,47 | 3 | 2,021 |
On the Frequency Bias of Generative Models | 27 | neurips | 2 | 0 | 2023-06-16 16:07:19.118000 | https://github.com/autonomousvision/frequency_bias | 36 | On the frequency bias of generative models | https://scholar.google.com/scholar?cluster=13416509158445417919&hl=en&as_sdt=0,14 | 9 | 2,021 |
Learning curves of generic features maps for realistic datasets with a teacher-student model | 47 | neurips | 3 | 0 | 2023-06-16 16:07:19.318000 | https://github.com/IdePHICS/GCMProject | 3 | Learning curves of generic features maps for realistic datasets with a teacher-student model | https://scholar.google.com/scholar?cluster=9914858716383445755&hl=en&as_sdt=0,31 | 2 | 2,021 |
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices | 10 | neurips | 2 | 1 | 2023-06-16 16:07:19.519000 | https://github.com/yandex-research/moshpit-sgd | 23 | Moshpit SGD: Communication-efficient decentralized training on heterogeneous unreliable devices | https://scholar.google.com/scholar?cluster=14221777398814396487&hl=en&as_sdt=0,18 | 2 | 2,021 |
Self-Supervised Learning Disentangled Group Representation as Feature | 30 | neurips | 8 | 1 | 2023-06-16 16:07:19.719000 | https://github.com/Wangt-CN/IP-IRM | 75 | Self-supervised learning disentangled group representation as feature | https://scholar.google.com/scholar?cluster=17449596620325468499&hl=en&as_sdt=0,14 | 4 | 2,021 |
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning | 2 | neurips | 0 | 0 | 2023-06-16 16:07:19.920000 | https://github.com/INK-USC/SalKG | 13 | Salkg: Learning from knowledge graph explanations for commonsense reasoning | https://scholar.google.com/scholar?cluster=6540484146067491013&hl=en&as_sdt=0,5 | 4 | 2,021 |
Conformal Bayesian Computation | 8 | neurips | 4 | 0 | 2023-06-16 16:07:20.121000 | https://github.com/edfong/conformal_bayes | 7 | Conformal bayesian computation | https://scholar.google.com/scholar?cluster=101153919231458556&hl=en&as_sdt=0,31 | 1 | 2,021 |
Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices | 7 | neurips | 1 | 0 | 2023-06-16 16:07:20.337000 | https://github.com/fedelopez77/gyrospd | 15 | Vector-valued distance and gyrocalculus on the space of symmetric positive definite matrices | https://scholar.google.com/scholar?cluster=12474792610041988042&hl=en&as_sdt=0,5 | 1 | 2,021 |
Improved Transformer for High-Resolution GANs | 48 | neurips | 7 | 4 | 2023-06-16 16:07:20.551000 | https://github.com/google-research/hit-gan | 85 | Improved transformer for high-resolution gans | https://scholar.google.com/scholar?cluster=13859013712282369139&hl=en&as_sdt=0,23 | 4 | 2,021 |
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence | 129 | neurips | 178 | 21 | 2023-06-16 16:07:20.752000 | https://github.com/yangxue0827/RotationDetection | 1,013 | Learning high-precision bounding box for rotated object detection via kullback-leibler divergence | https://scholar.google.com/scholar?cluster=15795399494869889077&hl=en&as_sdt=0,47 | 21 | 2,021 |
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling | 276 | neurips | 175 | 13 | 2023-06-16 16:07:20.952000 | https://github.com/torchssl/torchssl | 1,128 | Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling | https://scholar.google.com/scholar?cluster=3803828200228720306&hl=en&as_sdt=0,29 | 14 | 2,021 |
Relative Flatness and Generalization | 27 | neurips | 1 | 0 | 2023-06-16 16:07:21.152000 | https://github.com/kampmichael/RelativeFlatnessAndGeneralization | 5 | Relative flatness and generalization | https://scholar.google.com/scholar?cluster=16801711530011720213&hl=en&as_sdt=0,44 | 2 | 2,021 |
Towards Multi-Grained Explainability for Graph Neural Networks | 33 | neurips | 6 | 3 | 2023-06-16 16:07:21.352000 | https://github.com/wuyxin/refine | 54 | Towards multi-grained explainability for graph neural networks | https://scholar.google.com/scholar?cluster=17873571047667991497&hl=en&as_sdt=0,32 | 4 | 2,021 |
Behavior From the Void: Unsupervised Active Pre-Training | 96 | neurips | 46 | 16 | 2023-06-16 16:07:21.552000 | https://github.com/rll-research/url_benchmark | 290 | Behavior from the void: Unsupervised active pre-training | https://scholar.google.com/scholar?cluster=10900014046487526554&hl=en&as_sdt=0,44 | 7 | 2,021 |
Neural Distance Embeddings for Biological Sequences | 11 | neurips | 17 | 3 | 2023-06-16 16:07:21.752000 | https://github.com/gcorso/neuroseed | 63 | Neural distance embeddings for biological sequences | https://scholar.google.com/scholar?cluster=15398350307763828837&hl=en&as_sdt=0,5 | 2 | 2,021 |
Fitting summary statistics of neural data with a differentiable spiking network simulator | 5 | neurips | 0 | 0 | 2023-06-16 16:07:21.951000 | https://github.com/epfl-lcn/pub-bellec-wang-2021-sample-and-measure | 4 | Fitting summary statistics of neural data with a differentiable spiking network simulator | https://scholar.google.com/scholar?cluster=1562941632690587103&hl=en&as_sdt=0,21 | 6 | 2,021 |
All Tokens Matter: Token Labeling for Training Better Vision Transformers | 108 | neurips | 34 | 5 | 2023-06-16 16:07:22.152000 | https://github.com/zihangJiang/TokenLabeling | 401 | All tokens matter: Token labeling for training better vision transformers | https://scholar.google.com/scholar?cluster=2653381841404725442&hl=en&as_sdt=0,5 | 13 | 2,021 |
Partition and Code: learning how to compress graphs | 12 | neurips | 5 | 0 | 2023-06-16 16:07:22.352000 | https://github.com/gbouritsas/PnC | 22 | Partition and Code: learning how to compress graphs | https://scholar.google.com/scholar?cluster=850351115776622533&hl=en&as_sdt=0,5 | 1 | 2,021 |
Online Variational Filtering and Parameter Learning | 6 | neurips | 3 | 0 | 2023-06-16 16:07:22.552000 | https://github.com/andrew-cr/online_var_fil | 18 | Online variational filtering and parameter learning | https://scholar.google.com/scholar?cluster=17576462119960289899&hl=en&as_sdt=0,5 | 2 | 2,021 |
Heavy Ball Neural Ordinary Differential Equations | 33 | neurips | 2 | 1 | 2023-06-16 16:07:22.753000 | https://github.com/hedixia/heavyballnode | 12 | Heavy ball neural ordinary differential equations | https://scholar.google.com/scholar?cluster=7014300232807854100&hl=en&as_sdt=0,16 | 1 | 2,021 |
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios | 45 | neurips | 22 | 8 | 2023-06-16 16:07:22.953000 | https://github.com/decile-team/distil | 125 | Similar: Submodular information measures based active learning in realistic scenarios | https://scholar.google.com/scholar?cluster=1754543766939132313&hl=en&as_sdt=0,11 | 13 | 2,021 |
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning | 18 | neurips | 2 | 0 | 2023-06-16 16:07:23.160000 | https://github.com/danruod/fs-dgpm | 10 | Flattening sharpness for dynamic gradient projection memory benefits continual learning | https://scholar.google.com/scholar?cluster=11838604925888767745&hl=en&as_sdt=0,45 | 1 | 2,021 |
Taxonomizing local versus global structure in neural network loss landscapes | 9 | neurips | 8 | 0 | 2023-06-16 16:07:23.360000 | https://github.com/nsfzyzz/loss_landscape_taxonomy | 12 | Taxonomizing local versus global structure in neural network loss landscapes | https://scholar.google.com/scholar?cluster=3906442243364935062&hl=en&as_sdt=0,6 | 2 | 2,021 |
Learning Models for Actionable Recourse | 11 | neurips | 1 | 0 | 2023-06-16 16:07:23.560000 | https://github.com/alexisjihyeross/adversarial_recourse | 4 | Learning models for actionable recourse | https://scholar.google.com/scholar?cluster=328953936977118438&hl=en&as_sdt=0,5 | 2 | 2,021 |
EIGNN: Efficient Infinite-Depth Graph Neural Networks | 21 | neurips | 0 | 0 | 2023-06-16 16:07:23.761000 | https://github.com/liu-jc/eignn | 15 | Eignn: Efficient infinite-depth graph neural networks | https://scholar.google.com/scholar?cluster=8296752647036611693&hl=en&as_sdt=0,5 | 2 | 2,021 |
Federated Graph Classification over Non-IID Graphs | 53 | neurips | 6 | 1 | 2023-06-16 16:07:23.962000 | https://github.com/Oxfordblue7/GCFL | 30 | Federated graph classification over non-iid graphs | https://scholar.google.com/scholar?cluster=1741103138242476670&hl=en&as_sdt=0,5 | 1 | 2,021 |
Conflict-Averse Gradient Descent for Multi-task learning | 69 | neurips | 17 | 2 | 2023-06-16 16:07:24.164000 | https://github.com/cranial-xix/cagrad | 69 | Conflict-averse gradient descent for multi-task learning | https://scholar.google.com/scholar?cluster=8762775323920560478&hl=en&as_sdt=0,47 | 3 | 2,021 |
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate | 11 | neurips | 0 | 1 | 2023-06-16 16:07:24.367000 | https://github.com/xingyuansun/amorsyn | 3 | Amortized synthesis of constrained configurations using a differentiable surrogate | https://scholar.google.com/scholar?cluster=4270018087352708570&hl=en&as_sdt=0,5 | 1 | 2,021 |
Revisiting Deep Learning Models for Tabular Data | 189 | neurips | 19 | 0 | 2023-06-16 16:07:24.567000 | https://github.com/Yura52/tabular-dl-revisiting-models | 82 | Revisiting deep learning models for tabular data | https://scholar.google.com/scholar?cluster=9460438335911205282&hl=en&as_sdt=0,47 | 2 | 2,021 |
SOPE: Spectrum of Off-Policy Estimators | 5 | neurips | 0 | 0 | 2023-06-16 16:07:24.767000 | https://github.com/pearl-utexas/sope | 0 | Sope: Spectrum of off-policy estimators | https://scholar.google.com/scholar?cluster=16068674194932676119&hl=en&as_sdt=0,28 | 1 | 2,021 |
Label-Imbalanced and Group-Sensitive Classification under Overparameterization | 41 | neurips | 4 | 0 | 2023-06-16 16:07:24.967000 | https://github.com/orparask/VS-Loss | 11 | Label-imbalanced and group-sensitive classification under overparameterization | https://scholar.google.com/scholar?cluster=1004937566956517205&hl=en&as_sdt=0,5 | 1 | 2,021 |
Functional Regularization for Reinforcement Learning via Learned Fourier Features | 5 | neurips | 2 | 0 | 2023-06-16 16:07:25.166000 | https://github.com/alexlioralexli/learned-fourier-features | 13 | Functional regularization for reinforcement learning via learned fourier features | https://scholar.google.com/scholar?cluster=9954772889129922226&hl=en&as_sdt=0,5 | 1 | 2,021 |
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity | 26 | neurips | 0 | 0 | 2023-06-16 16:07:25.369000 | https://github.com/hugobb/StochasticGamesOpt | 2 | Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity | https://scholar.google.com/scholar?cluster=17964943870757537526&hl=en&as_sdt=0,11 | 1 | 2,021 |
Adversarial Robustness with Non-uniform Perturbations | 18 | neurips | 1 | 2 | 2023-06-16 16:07:25.569000 | https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations | 6 | Adversarial robustness with non-uniform perturbations | https://scholar.google.com/scholar?cluster=18104312853513215625&hl=en&as_sdt=0,3 | 1 | 2,021 |
Container: Context Aggregation Networks | 43 | neurips | 9 | 1 | 2023-06-16 16:07:25.770000 | https://github.com/allenai/container | 50 | Container: Context aggregation network | https://scholar.google.com/scholar?cluster=16108325035684916829&hl=en&as_sdt=0,33 | 7 | 2,021 |
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs | 53 | neurips | 5 | 0 | 2023-06-16 16:07:25.970000 | https://github.com/miralab-ustc/qe-cone | 37 | Cone: Cone embeddings for multi-hop reasoning over knowledge graphs | https://scholar.google.com/scholar?cluster=5827447506363072334&hl=en&as_sdt=0,14 | 2 | 2,021 |
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time | 6 | neurips | 2 | 1 | 2023-06-16 16:07:26.169000 | https://github.com/anshuln/training-for-the-future | 6 | Training for the future: A simple gradient interpolation loss to generalize along time | https://scholar.google.com/scholar?cluster=10591578379838672358&hl=en&as_sdt=0,11 | 2 | 2,021 |
Agent Modelling under Partial Observability for Deep Reinforcement Learning | 25 | neurips | 6 | 3 | 2023-06-16 16:07:26.370000 | https://github.com/uoe-agents/LIAM | 23 | Agent modelling under partial observability for deep reinforcement learning | https://scholar.google.com/scholar?cluster=12719717848124946591&hl=en&as_sdt=0,5 | 4 | 2,021 |
Conservative Offline Distributional Reinforcement Learning | 39 | neurips | 5 | 0 | 2023-06-16 16:07:26.570000 | https://github.com/JasonMa2016/CODAC | 11 | Conservative offline distributional reinforcement learning | https://scholar.google.com/scholar?cluster=15495713229557963768&hl=en&as_sdt=0,5 | 2 | 2,021 |
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks | 2 | neurips | 1 | 2 | 2023-06-16 16:07:26.771000 | https://github.com/machanic/tangentattack | 15 | Finding optimal tangent points for reducing distortions of hard-label attacks | https://scholar.google.com/scholar?cluster=2435511386343410332&hl=en&as_sdt=0,3 | 2 | 2,021 |
Scalable Diverse Model Selection for Accessible Transfer Learning | 13 | neurips | 3 | 1 | 2023-06-16 16:07:26.971000 | https://github.com/dbolya/parc | 14 | Scalable diverse model selection for accessible transfer learning | https://scholar.google.com/scholar?cluster=3499493758042195459&hl=en&as_sdt=0,5 | 1 | 2,021 |
Fine-Grained Zero-Shot Learning with DNA as Side Information | 11 | neurips | 3 | 1 | 2023-06-16 16:07:27.171000 | https://github.com/sbadirli/Fine-Grained-ZSL-with-DNA | 7 | Fine-grained zero-shot learning with dna as side information | https://scholar.google.com/scholar?cluster=5909945614319311609&hl=en&as_sdt=0,5 | 1 | 2,021 |
Scheduling jobs with stochastic holding costs | 2 | neurips | 0 | 0 | 2023-06-16 16:07:27.371000 | https://github.com/learning-to-schedule/learning-to-schedule | 3 | Scheduling jobs with stochastic holding costs | https://scholar.google.com/scholar?cluster=2702725393229126199&hl=en&as_sdt=0,18 | 1 | 2,021 |
Exploiting a Zoo of Checkpoints for Unseen Tasks | 3 | neurips | 1 | 1 | 2023-06-16 16:07:27.571000 | https://github.com/baidu-research/task_space | 9 | Exploiting a zoo of checkpoints for unseen tasks | https://scholar.google.com/scholar?cluster=4042561766012812832&hl=en&as_sdt=0,36 | 2 | 2,021 |
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach | 9 | neurips | 10 | 0 | 2023-06-16 16:07:27.779000 | https://github.com/qitianwu/FATE | 18 | Towards open-world feature extrapolation: An inductive graph learning approach | https://scholar.google.com/scholar?cluster=5551821224850406446&hl=en&as_sdt=0,5 | 2 | 2,021 |
Stochastic bandits with groups of similar arms. | 4 | neurips | 0 | 0 | 2023-06-16 16:07:27.980000 | https://github.com/fabienpesquerel/stochastic-bandits-with-groups-of-similar-arms-neurips-2021 | 0 | Stochastic bandits with groups of similar arms. | https://scholar.google.com/scholar?cluster=5844900148532938512&hl=en&as_sdt=0,5 | 2 | 2,021 |
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds | 6 | neurips | 1 | 0 | 2023-06-16 16:07:28.179000 | https://github.com/samgregoost/Rethinking-CGANs | 18 | Rethinking conditional GAN training: An approach using geometrically structured latent manifolds | https://scholar.google.com/scholar?cluster=11263236703274984853&hl=en&as_sdt=0,47 | 1 | 2,021 |
Fast Axiomatic Attribution for Neural Networks | 11 | neurips | 1 | 0 | 2023-06-16 16:07:28.379000 | https://github.com/visinf/fast-axiomatic-attribution | 13 | Fast axiomatic attribution for neural networks | https://scholar.google.com/scholar?cluster=1107042055315452910&hl=en&as_sdt=0,5 | 3 | 2,021 |
Compressive Visual Representations | 29 | neurips | 6 | 0 | 2023-06-16 16:07:28.578000 | https://github.com/google-research/compressive-visual-representations | 33 | Compressive visual representations | https://scholar.google.com/scholar?cluster=10272376959092579040&hl=en&as_sdt=0,5 | 6 | 2,021 |
Grounding inductive biases in natural images: invariance stems from variations in data | 11 | neurips | 2 | 0 | 2023-06-16 16:07:28.778000 | https://github.com/facebookresearch/grounding-inductive-biases | 15 | Grounding inductive biases in natural images: invariance stems from variations in data | https://scholar.google.com/scholar?cluster=1326104865279107103&hl=en&as_sdt=0,11 | 15 | 2,021 |
Directed Graph Contrastive Learning | 28 | neurips | 1 | 0 | 2023-06-16 16:07:28.978000 | https://github.com/flyingtango/digcl | 33 | Directed graph contrastive learning | https://scholar.google.com/scholar?cluster=8884605387451104351&hl=en&as_sdt=0,14 | 2 | 2,021 |
Space-time Mixing Attention for Video Transformer | 64 | neurips | 7 | 1 | 2023-06-16 16:07:29.179000 | https://github.com/1adrianb/video-transformers | 37 | Space-time mixing attention for video transformer | https://scholar.google.com/scholar?cluster=13067561196339094187&hl=en&as_sdt=0,5 | 3 | 2,021 |
Only Train Once: A One-Shot Neural Network Training And Pruning Framework | 36 | neurips | 27 | 3 | 2023-06-16 16:07:29.379000 | https://github.com/tianyic/only_train_once | 185 | Only train once: A one-shot neural network training and pruning framework | https://scholar.google.com/scholar?cluster=10322314510418461770&hl=en&as_sdt=0,38 | 9 | 2,021 |
Referring Transformer: A One-step Approach to Multi-task Visual Grounding | 43 | neurips | 0 | 2 | 2023-06-16 16:07:29.579000 | https://github.com/ubc-vision/RefTR | 49 | Referring transformer: A one-step approach to multi-task visual grounding | https://scholar.google.com/scholar?cluster=5171653636072617077&hl=en&as_sdt=0,47 | 2 | 2,021 |
Decoupling the Depth and Scope of Graph Neural Networks | 62 | neurips | 18 | 2 | 2023-06-16 16:07:29.779000 | https://github.com/facebookresearch/shaDow_GNN | 120 | Decoupling the depth and scope of graph neural networks | https://scholar.google.com/scholar?cluster=584581848788200255&hl=en&as_sdt=0,5 | 8 | 2,021 |
Knowledge-Adaptation Priors | 12 | neurips | 2 | 0 | 2023-06-16 16:07:29.980000 | https://github.com/team-approx-bayes/kpriors | 13 | Knowledge-adaptation priors | https://scholar.google.com/scholar?cluster=8988283742772208306&hl=en&as_sdt=0,21 | 3 | 2,021 |
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting | 6 | neurips | 5 | 0 | 2023-06-16 16:07:30.184000 | https://github.com/AdityaLab/EpiFNP | 8 | When in doubt: Neural non-parametric uncertainty quantification for epidemic forecasting | https://scholar.google.com/scholar?cluster=17960456956653217453&hl=en&as_sdt=0,26 | 3 | 2,021 |
CogView: Mastering Text-to-Image Generation via Transformers | 264 | neurips | 165 | 17 | 2023-06-16 16:07:30.389000 | https://github.com/THUDM/CogView | 1,407 | Cogview: Mastering text-to-image generation via transformers | https://scholar.google.com/scholar?cluster=11027183169038977124&hl=en&as_sdt=0,5 | 54 | 2,021 |
Algorithmic stability and generalization of an unsupervised feature selection algorithm | 6 | neurips | 3 | 0 | 2023-06-16 16:07:30.590000 | https://github.com/xinxingwu-uk/ufs | 11 | Algorithmic stability and generalization of an unsupervised feature selection algorithm | https://scholar.google.com/scholar?cluster=4215442541318442618&hl=en&as_sdt=0,41 | 1 | 2,021 |
Matching a Desired Causal State via Shift Interventions | 9 | neurips | 1 | 0 | 2023-06-16 16:07:30.791000 | https://github.com/uhlerlab/causal_mean_matching | 5 | Matching a desired causal state via shift interventions | https://scholar.google.com/scholar?cluster=9050178103753857442&hl=en&as_sdt=0,33 | 1 | 2,021 |
Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport | 9 | neurips | 3 | 0 | 2023-06-16 16:07:30.991000 | https://github.com/hsinyilin19/discriminator-constrained-optimal-transport-network | 21 | Unsupervised noise adaptive speech enhancement by discriminator-constrained optimal transport | https://scholar.google.com/scholar?cluster=334924147335137151&hl=en&as_sdt=0,5 | 1 | 2,021 |
Policy Learning Using Weak Supervision | 4 | neurips | 1 | 0 | 2023-06-16 16:07:31.209000 | https://github.com/wangjksjtu/peerpl | 1 | Policy learning using weak supervision | https://scholar.google.com/scholar?cluster=16464632898961524841&hl=en&as_sdt=0,18 | 2 | 2,021 |
Chasing Sparsity in Vision Transformers: An End-to-End Exploration | 94 | neurips | 11 | 2 | 2023-06-16 16:07:31.409000 | https://github.com/VITA-Group/SViTE | 75 | Chasing sparsity in vision transformers: An end-to-end exploration | https://scholar.google.com/scholar?cluster=12875590970833854171&hl=en&as_sdt=0,44 | 14 | 2,021 |
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis | 44 | neurips | 19 | 3 | 2023-06-16 16:07:31.611000 | https://github.com/xingangpan/shadegan | 127 | A shading-guided generative implicit model for shape-accurate 3d-aware image synthesis | https://scholar.google.com/scholar?cluster=10440624240411040295&hl=en&as_sdt=0,3 | 15 | 2,021 |
Row-clustering of a Point Process-valued Matrix | 2 | neurips | 2 | 1 | 2023-06-16 16:07:31.811000 | https://github.com/lihaoyin/mmmpp | 0 | Row-clustering of a Point Process-valued Matrix | https://scholar.googleusercontent.com/scholar?q=cache:psVjPBSJPQwJ:scholar.google.com/+Row-clustering+of+a+Point+Process-valued+Matrix&hl=en&as_sdt=0,47 | 1 | 2,021 |
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information | 11 | neurips | 4 | 6 | 2023-06-16 16:07:32.011000 | https://github.com/camp-explain-ai/inputiba | 27 | Fine-grained neural network explanation by identifying input features with predictive information | https://scholar.google.com/scholar?cluster=13775392989196455147&hl=en&as_sdt=0,5 | 5 | 2,021 |
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints | 38 | neurips | 3 | 0 | 2023-06-16 16:07:32.213000 | https://github.com/pralab/Fast-Minimum-Norm-FMN-Attack | 19 | Fast minimum-norm adversarial attacks through adaptive norm constraints | https://scholar.google.com/scholar?cluster=1895780984587172600&hl=en&as_sdt=0,4 | 3 | 2,021 |
Uncertainty Quantification and Deep Ensembles | 66 | neurips | 1 | 1 | 2023-06-16 16:07:32.413000 | https://github.com/rahulrahaman/Uncertainty-Quantification-and-Deep-Ensemble | 5 | Uncertainty quantification and deep ensembles | https://scholar.google.com/scholar?cluster=429015783640365674&hl=en&as_sdt=0,8 | 1 | 2,021 |
Directed Probabilistic Watershed | 0 | neurips | 0 | 0 | 2023-06-16 16:07:32.614000 | https://github.com/hci-unihd/directed_probabilistic_watershed | 0 | Directed Probabilistic Watershed | https://scholar.google.com/scholar?cluster=2806900364390366919&hl=en&as_sdt=0,20 | 1 | 2,021 |
Explicable Reward Design for Reinforcement Learning Agents | 18 | neurips | 3 | 0 | 2023-06-16 16:07:32.814000 | https://github.com/adishs/neurips2021_explicable-reward-design_code | 3 | Explicable reward design for reinforcement learning agents | https://scholar.google.com/scholar?cluster=150260483940462803&hl=en&as_sdt=0,33 | 1 | 2,021 |
A Minimalist Approach to Offline Reinforcement Learning | 267 | neurips | 36 | 2 | 2023-06-16 16:07:33.016000 | https://github.com/sfujim/TD3_BC | 228 | A minimalist approach to offline reinforcement learning | https://scholar.google.com/scholar?cluster=1743052010402400643&hl=en&as_sdt=0,33 | 4 | 2,021 |
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs | 7 | neurips | 1 | 0 | 2023-06-16 16:07:33.217000 | https://github.com/raulastudillo06/budgetedbo | 7 | Multi-step budgeted bayesian optimization with unknown evaluation costs | https://scholar.google.com/scholar?cluster=11066965782601470103&hl=en&as_sdt=0,5 | 3 | 2,021 |
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