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A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | 4 | neurips | 2 | 0 | 2023-06-16 22:57:32.928000 | https://github.com/starrskyy/fedgda-gt | 2 | A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | https://scholar.google.com/scholar?cluster=7833139237183266538&hl=en&as_sdt=0,23 | 1 | 2,022 |
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline | 21 | neurips | 0 | 0 | 2023-06-16 22:57:33.138000 | https://github.com/OpenPerceptionX/TCP | 1 | Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline | https://scholar.google.com/scholar?cluster=1817675006219450608&hl=en&as_sdt=0,5 | 2 | 2,022 |
Falsification before Extrapolation in Causal Effect Estimation | 1 | neurips | 0 | 0 | 2023-06-16 22:57:33.349000 | https://github.com/clinicalml/rct-obs-extrapolation | 1 | Falsification before Extrapolation in Causal Effect Estimation | https://scholar.google.com/scholar?cluster=958040257149340285&hl=en&as_sdt=0,5 | 8 | 2,022 |
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG | 5 | neurips | 2 | 0 | 2023-06-16 22:57:33.561000 | https://github.com/rkobler/TSMNet | 22 | SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG | https://scholar.google.com/scholar?cluster=18096469291943406428&hl=en&as_sdt=0,10 | 1 | 2,022 |
Semantic uncertainty intervals for disentangled latent spaces | 6 | neurips | 0 | 0 | 2023-06-16 22:57:33.771000 | https://github.com/swamiviv/CLASP | 2 | Semantic uncertainty intervals for disentangled latent spaces | https://scholar.google.com/scholar?cluster=15336613379158293365&hl=en&as_sdt=0,5 | 1 | 2,022 |
Meta-Learning with Self-Improving Momentum Target | 1 | neurips | 1 | 0 | 2023-06-16 22:57:33.982000 | https://github.com/jihoontack/SiMT | 23 | Meta-Learning with Self-Improving Momentum Target | https://scholar.google.com/scholar?cluster=8856141874430455067&hl=en&as_sdt=0,36 | 2 | 2,022 |
On the Robustness of Graph Neural Diffusion to Topology Perturbations | 6 | neurips | 1 | 0 | 2023-06-16 22:57:34.192000 | https://github.com/zknus/robustness-of-graph-neural-diffusion | 10 | On the robustness of graph neural diffusion to topology perturbations | https://scholar.google.com/scholar?cluster=12358515421385829046&hl=en&as_sdt=0,11 | 2 | 2,022 |
Few-shot Relational Reasoning via Connection Subgraph Pretraining | 12 | neurips | 5 | 4 | 2023-06-16 22:57:34.404000 | https://github.com/snap-stanford/csr | 20 | Few-shot Relational Reasoning via Connection Subgraph Pretraining | https://scholar.google.com/scholar?cluster=7808961295486020115&hl=en&as_sdt=0,5 | 4 | 2,022 |
Equivariant Networks for Zero-Shot Coordination | 3 | neurips | 0 | 0 | 2023-06-16 22:57:34.616000 | https://github.com/gfppoy/equivariant-zsc | 1 | Equivariant networks for zero-shot coordination | https://scholar.google.com/scholar?cluster=8378470160963031417&hl=en&as_sdt=0,5 | 1 | 2,022 |
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability | 0 | neurips | 0 | 1 | 2023-06-16 22:57:34.828000 | https://github.com/wyjung0625/qcpo | 2 | Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability | https://scholar.google.com/scholar?cluster=2759019976865790748&hl=en&as_sdt=0,44 | 1 | 2,022 |
Procedural Image Programs for Representation Learning | 1 | neurips | 2 | 0 | 2023-06-16 22:57:35.039000 | https://github.com/mbaradad/shaders21k | 19 | Procedural Image Programs for Representation Learning | https://scholar.google.com/scholar?cluster=9270170976993930491&hl=en&as_sdt=0,5 | 1 | 2,022 |
Motion Transformer with Global Intention Localization and Local Movement Refinement | 15 | neurips | 46 | 4 | 2023-06-16 22:57:35.250000 | https://github.com/sshaoshuai/mtr | 349 | Motion transformer with global intention localization and local movement refinement | https://scholar.google.com/scholar?cluster=17050187484850062043&hl=en&as_sdt=0,18 | 28 | 2,022 |
Conformal Frequency Estimation with Sketched Data | 1 | neurips | 1 | 0 | 2023-06-16 22:57:35.462000 | https://github.com/msesia/conformalized-sketching | 3 | Conformal Frequency Estimation with Sketched Data | https://scholar.google.com/scholar?cluster=9560083140059478955&hl=en&as_sdt=0,5 | 1 | 2,022 |
Revisiting Active Sets for Gaussian Process Decoders | 1 | neurips | 1 | 0 | 2023-06-16 22:57:35.672000 | https://github.com/pmorenoz/SASGP | 4 | Revisiting active sets for Gaussian process decoders | https://scholar.google.com/scholar?cluster=2795726720266164112&hl=en&as_sdt=0,5 | 1 | 2,022 |
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness | 1 | neurips | 1 | 0 | 2023-06-16 22:57:35.883000 | https://github.com/mccree177/amp | 26 | AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness | https://scholar.google.com/scholar?cluster=4092261735524740694&hl=en&as_sdt=0,5 | 1 | 2,022 |
CyCLIP: Cyclic Contrastive Language-Image Pretraining | 33 | neurips | 6 | 1 | 2023-06-16 22:57:36.095000 | https://github.com/goel-shashank/CyCLIP | 84 | Cyclip: Cyclic contrastive language-image pretraining | https://scholar.google.com/scholar?cluster=7059915234869339584&hl=en&as_sdt=0,14 | 5 | 2,022 |
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet | 15 | neurips | 1 | 0 | 2023-06-16 22:57:36.306000 | https://github.com/google-research/imagenet-mistakes | 15 | When does dough become a bagel? analyzing the remaining mistakes on imagenet | https://scholar.google.com/scholar?cluster=8522271283148753556&hl=en&as_sdt=0,5 | 3 | 2,022 |
Non-deep Networks | 36 | neurips | 42 | 7 | 2023-06-16 22:57:36.517000 | https://github.com/imankgoyal/NonDeepNetworks | 577 | Non-deep networks | https://scholar.google.com/scholar?cluster=16588786431597949156&hl=en&as_sdt=0,41 | 47 | 2,022 |
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation | 3 | neurips | 0 | 0 | 2023-06-16 22:57:36.728000 | https://github.com/kristian-georgiev/privacy-induces-robustness | 1 | Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation | https://scholar.google.com/scholar?cluster=14209092131686935951&hl=en&as_sdt=0,5 | 1 | 2,022 |
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel | 1 | neurips | 0 | 0 | 2023-06-16 22:57:36.939000 | https://github.com/changyong-oh/law2order | 1 | Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel | https://scholar.google.com/scholar?cluster=14203375121421572867&hl=en&as_sdt=0,10 | 1 | 2,022 |
Positively Weighted Kernel Quadrature via Subsampling | 10 | neurips | 0 | 0 | 2023-06-16 22:57:37.149000 | https://github.com/satoshi-hayakawa/kernel-quadrature | 4 | Positively weighted kernel quadrature via subsampling | https://scholar.google.com/scholar?cluster=16160100637122636412&hl=en&as_sdt=0,39 | 1 | 2,022 |
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics | 1 | neurips | 4 | 0 | 2023-06-16 22:57:37.376000 | https://github.com/tum-pbs/dmcf | 31 | Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics | https://scholar.google.com/scholar?cluster=5915590166499828539&hl=en&as_sdt=0,31 | 3 | 2,022 |
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty | 0 | neurips | 0 | 0 | 2023-06-16 22:57:37.587000 | https://github.com/luningsun/splinelearningequation | 3 | Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty | https://scholar.google.com/scholar?cluster=7412491486510109194&hl=en&as_sdt=0,10 | 3 | 2,022 |
FR: Folded Rationalization with a Unified Encoder | 3 | neurips | 0 | 0 | 2023-06-16 22:57:37.797000 | https://github.com/jugechengzi/fr | 9 | FR: Folded Rationalization with a Unified Encoder | https://scholar.google.com/scholar?cluster=17701298430512519187&hl=en&as_sdt=0,10 | 2 | 2,022 |
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification | 0 | neurips | 0 | 0 | 2023-06-16 22:57:38.008000 | https://github.com/akimotolab/m2td3 | 1 | Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification | https://scholar.google.com/scholar?cluster=2762930312674513633&hl=en&as_sdt=0,3 | 0 | 2,022 |
When Does Group Invariant Learning Survive Spurious Correlations? | 4 | neurips | 0 | 0 | 2023-06-16 22:57:38.219000 | https://github.com/beastlyprime/group-invariant-learning | 3 | When Does Group Invariant Learning Survive Spurious Correlations? | https://scholar.google.com/scholar?cluster=16534284812687563601&hl=en&as_sdt=0,10 | 1 | 2,022 |
SNAKE: Shape-aware Neural 3D Keypoint Field | 0 | neurips | 5 | 0 | 2023-06-16 22:57:38.432000 | https://github.com/zhongcl-thu/snake | 199 | SNAKE: Shape-aware Neural 3D Keypoint Field | https://scholar.google.com/scholar?cluster=16201409541555687414&hl=en&as_sdt=0,5 | 5 | 2,022 |
Minimax Optimal Online Imitation Learning via Replay Estimation | 2 | neurips | 1 | 0 | 2023-06-16 22:57:38.643000 | https://github.com/gkswamy98/replay_est | 2 | Minimax optimal online imitation learning via replay estimation | https://scholar.google.com/scholar?cluster=17967164041276198597&hl=en&as_sdt=0,10 | 3 | 2,022 |
Multi-layer State Evolution Under Random Convolutional Design | 0 | neurips | 1 | 0 | 2023-06-16 22:57:38.853000 | https://github.com/mdnls/conv-ml-amp | 0 | Multi-layer State Evolution Under Random Convolutional Design | https://scholar.google.com/scholar?cluster=10470374566280377653&hl=en&as_sdt=0,33 | 1 | 2,022 |
GULP: a prediction-based metric between representations | 1 | neurips | 0 | 0 | 2023-06-16 22:57:39.064000 | https://github.com/sgstepaniants/gulp | 5 | GULP: a prediction-based metric between representations | https://scholar.google.com/scholar?cluster=17478835353985668968&hl=en&as_sdt=0,5 | 3 | 2,022 |
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks | 0 | neurips | 0 | 1 | 2023-06-16 22:57:39.275000 | https://github.com/shariqiqbal2810/alma | 14 | ALMA: Hierarchical Learning for Composite Multi-Agent Tasks | https://scholar.google.com/scholar?cluster=3111894008525567959&hl=en&as_sdt=0,36 | 1 | 2,022 |
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions | 4 | neurips | 0 | 0 | 2023-06-16 22:57:39.486000 | https://github.com/kjason/cpwl2relunetwork | 0 | Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions | https://scholar.google.com/scholar?cluster=6114292183557641648&hl=en&as_sdt=0,5 | 1 | 2,022 |
Assaying Out-Of-Distribution Generalization in Transfer Learning | 19 | neurips | 0 | 0 | 2023-06-16 22:57:39.697000 | https://github.com/amazon-research/assaying-ood | 10 | Assaying out-of-distribution generalization in transfer learning | https://scholar.google.com/scholar?cluster=2028336304446280911&hl=en&as_sdt=0,34 | 6 | 2,022 |
Learning Interface Conditions in Domain Decomposition Solvers | 3 | neurips | 0 | 0 | 2023-06-16 22:57:39.907000 | https://github.com/compdyn/learning-oras | 2 | Learning interface conditions in domain decomposition solvers | https://scholar.google.com/scholar?cluster=7720297619688650714&hl=en&as_sdt=0,5 | 2 | 2,022 |
Hamiltonian Latent Operators for content and motion disentanglement in image sequences | 0 | neurips | 0 | 0 | 2023-06-16 22:57:40.118000 | https://github.com/mdasifkhan/halo | 1 | Hamiltonian Latent Operators for content and motion disentanglement in image sequences | https://scholar.google.com/scholar?cluster=3449357233115494687&hl=en&as_sdt=0,14 | 2 | 2,022 |
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited | 14 | neurips | 4 | 0 | 2023-06-16 22:57:40.329000 | https://github.com/ivam-he/chebnetii | 16 | Convolutional neural networks on graphs with chebyshev approximation, revisited | https://scholar.google.com/scholar?cluster=8441578707111569242&hl=en&as_sdt=0,33 | 3 | 2,022 |
A Kernelised Stein Statistic for Assessing Implicit Generative Models | 1 | neurips | 0 | 0 | 2023-06-16 22:57:40.541000 | https://github.com/wenkaixl/npksd | 2 | A kernelised Stein statistic for assessing implicit generative models | https://scholar.google.com/scholar?cluster=18442369245856609106&hl=en&as_sdt=0,39 | 1 | 2,022 |
Fine-Grained Semantically Aligned Vision-Language Pre-Training | 12 | neurips | 1 | 5 | 2023-06-16 22:57:40.752000 | https://github.com/yyjmjc/loupe | 34 | Fine-grained semantically aligned vision-language pre-training | https://scholar.google.com/scholar?cluster=238317474783907025&hl=en&as_sdt=0,5 | 8 | 2,022 |
Outlier-Robust Sparse Estimation via Non-Convex Optimization | 11 | neurips | 0 | 0 | 2023-06-16 22:57:40.963000 | https://github.com/guptashvm/sparse-gd | 0 | Outlier-robust sparse estimation via non-convex optimization | https://scholar.google.com/scholar?cluster=8059244212591008232&hl=en&as_sdt=0,39 | 1 | 2,022 |
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning | 0 | neurips | 0 | 0 | 2023-06-16 22:57:41.174000 | https://github.com/liuyejia/gps_cl | 0 | Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning | https://scholar.google.com/scholar?cluster=6762628467691411042&hl=en&as_sdt=0,34 | 1 | 2,022 |
Contact-aware Human Motion Forecasting | 2 | neurips | 0 | 0 | 2023-06-16 22:57:41.385000 | https://github.com/wei-mao-2019/contawaremotionpred | 18 | Contact-aware human motion forecasting | https://scholar.google.com/scholar?cluster=4638557404830348541&hl=en&as_sdt=0,5 | 2 | 2,022 |
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer | 6 | neurips | 1,520 | 270 | 2023-06-16 22:57:41.596000 | https://github.com/PaddlePaddle/PaddleSeg | 7,245 | RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer | https://scholar.google.com/scholar?cluster=9262270613134229&hl=en&as_sdt=0,23 | 84 | 2,022 |
Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks | 3 | neurips | 1 | 0 | 2023-06-16 22:57:41.807000 | https://github.com/ylhz/natural-color-fool | 19 | Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks | https://scholar.google.com/scholar?cluster=1908653488262515792&hl=en&as_sdt=0,5 | 1 | 2,022 |
Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again | 0 | neurips | 0 | 1 | 2023-06-16 22:57:42.018000 | https://github.com/vita-group/gradientgcn | 7 | Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again | https://scholar.google.com/scholar?cluster=11879351906859238595&hl=en&as_sdt=0,5 | 10 | 2,022 |
Egocentric Video-Language Pretraining | 7 | neurips | 16 | 3 | 2023-06-16 22:57:42.229000 | https://github.com/showlab/egovlp | 153 | Egocentric video-language pretraining | https://scholar.google.com/scholar?cluster=13386829043972751350&hl=en&as_sdt=0,5 | 5 | 2,022 |
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations | 0 | neurips | 0 | 0 | 2023-06-16 22:57:42.440000 | https://github.com/289371298/ceip | 0 | CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations | https://scholar.google.com/scholar?cluster=12367064193622872891&hl=en&as_sdt=0,37 | 0 | 2,022 |
LAMP: Extracting Text from Gradients with Language Model Priors | 6 | neurips | 5 | 0 | 2023-06-16 22:57:42.651000 | https://github.com/eth-sri/lamp | 14 | Lamp: Extracting text from gradients with language model priors | https://scholar.google.com/scholar?cluster=6444993593639997976&hl=en&as_sdt=0,11 | 6 | 2,022 |
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms | 7 | neurips | 0 | 0 | 2023-06-16 22:57:42.863000 | https://github.com/abhishekpanigrahi1996/Adaptive-SDE | 0 | On the SDEs and scaling rules for adaptive gradient algorithms | https://scholar.google.com/scholar?cluster=81871230063577322&hl=en&as_sdt=0,5 | 1 | 2,022 |
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement | 3 | neurips | 378 | 170 | 2023-06-16 22:57:43.074000 | https://github.com/facebookresearch/habitat-lab | 1,109 | VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement | https://scholar.google.com/scholar?cluster=6680559903388090895&hl=en&as_sdt=0,50 | 43 | 2,022 |
Evaluating Graph Generative Models with Contrastively Learned Features | 3 | neurips | 0 | 0 | 2023-06-16 22:57:43.284000 | https://github.com/hamed1375/self-supervised-models-for-ggm-evaluation | 3 | Evaluating Graph Generative Models with Contrastively Learned Features | https://scholar.google.com/scholar?cluster=11402654840281713194&hl=en&as_sdt=0,23 | 1 | 2,022 |
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning | 8 | neurips | 0 | 0 | 2023-06-16 22:57:43.495000 | https://github.com/taoqi98/fairvfl | 4 | Fairvfl: A fair vertical federated learning framework with contrastive adversarial learning | https://scholar.google.com/scholar?cluster=8028849683969991301&hl=en&as_sdt=0,5 | 1 | 2,022 |
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering | 7 | neurips | 1 | 2 | 2023-06-16 22:57:43.707000 | https://github.com/anzhang314/bc-loss | 19 | Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering | https://scholar.google.com/scholar?cluster=17056519215023278484&hl=en&as_sdt=0,7 | 2 | 2,022 |
A Consistent and Differentiable Lp Canonical Calibration Error Estimator | 10 | neurips | 0 | 0 | 2023-06-16 22:57:43.918000 | https://github.com/tpopordanoska/ece-kde | 5 | A consistent and differentiable lp canonical calibration error estimator | https://scholar.google.com/scholar?cluster=1430371157106751705&hl=en&as_sdt=0,33 | 1 | 2,022 |
Transform Once: Efficient Operator Learning in Frequency Domain | 4 | neurips | 1 | 1 | 2023-06-16 22:57:44.128000 | https://github.com/diffeqml/kairos | 12 | Transform once: Efficient operator learning in frequency domain | https://scholar.google.com/scholar?cluster=5960111959260104318&hl=en&as_sdt=0,44 | 3 | 2,022 |
A Solver-free Framework for Scalable Learning in Neural ILP Architectures | 0 | neurips | 1 | 0 | 2023-06-16 22:57:44.340000 | https://github.com/dair-iitd/ilploss | 8 | A Solver-Free Framework for Scalable Learning in Neural ILP Architectures | https://scholar.google.com/scholar?cluster=10416996433754364895&hl=en&as_sdt=0,44 | 4 | 2,022 |
High-dimensional Additive Gaussian Processes under Monotonicity Constraints | 2 | neurips | 0 | 0 | 2023-06-16 22:57:44.551000 | https://github.com/anfelopera/lineqGPR | 5 | High-dimensional additive Gaussian processes under monotonicity constraints | https://scholar.google.com/scholar?cluster=806848619663910077&hl=en&as_sdt=0,6 | 4 | 2,022 |
Spherical Channels for Modeling Atomic Interactions | 8 | neurips | 163 | 18 | 2023-06-16 22:57:44.763000 | https://github.com/Open-Catalyst-Project/ocp | 411 | Spherical channels for modeling atomic interactions | https://scholar.google.com/scholar?cluster=11935092226375810491&hl=en&as_sdt=0,47 | 24 | 2,022 |
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning | 6 | neurips | 1 | 0 | 2023-06-16 22:57:44.976000 | https://github.com/hbzju/solar | 21 | SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning | https://scholar.google.com/scholar?cluster=10356040081332575576&hl=en&as_sdt=0,41 | 1 | 2,022 |
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels | 0 | neurips | 0 | 0 | 2023-06-16 22:57:45.190000 | https://github.com/mkc1000/btgp | 3 | Log-Linear-Time Gaussian Processes Using Binary Tree Kernels | https://scholar.google.com/scholar?cluster=7844571481684303154&hl=en&as_sdt=0,10 | 1 | 2,022 |
Recovering Private Text in Federated Learning of Language Models | 6 | neurips | 6 | 3 | 2023-06-16 22:57:45.450000 | https://github.com/princeton-sysml/film | 37 | Recovering private text in federated learning of language models | https://scholar.google.com/scholar?cluster=12587257399289185667&hl=en&as_sdt=0,5 | 4 | 2,022 |
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation | 7 | neurips | 1 | 1 | 2023-06-16 22:57:45.662000 | https://github.com/ictnlp/nmla-nat | 18 | Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation | https://scholar.google.com/scholar?cluster=12848987996954988542&hl=en&as_sdt=0,37 | 2 | 2,022 |
Learning Deep Input-Output Stable Dynamics | 1 | neurips | 0 | 0 | 2023-06-16 22:57:45.873000 | https://github.com/clinfo/deepiostability | 4 | Learning Deep Input-Output Stable Dynamics | https://scholar.google.com/scholar?cluster=12258814525562910843&hl=en&as_sdt=0,10 | 3 | 2,022 |
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems | 1 | neurips | 5 | 0 | 2023-06-16 22:57:46.084000 | https://github.com/ericyangyu/pocar | 5 | Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems | https://scholar.google.com/scholar?cluster=14223207610228521971&hl=en&as_sdt=0,44 | 1 | 2,022 |
Gradient Descent: The Ultimate Optimizer | 17 | neurips | 20 | 1 | 2023-06-16 22:57:46.295000 | https://github.com/kach/gradient-descent-the-ultimate-optimizer | 328 | Gradient descent: The ultimate optimizer | https://scholar.google.com/scholar?cluster=5346772952705282375&hl=en&as_sdt=0,44 | 4 | 2,022 |
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization | 8 | neurips | 8 | 0 | 2023-06-16 22:57:46.507000 | https://github.com/kevinsbello/dagma | 33 | DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization | https://scholar.google.com/scholar?cluster=8930082693367383470&hl=en&as_sdt=0,44 | 3 | 2,022 |
Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization | 27 | neurips | 2 | 2 | 2023-06-16 22:57:46.718000 | https://github.com/salesforce/ensemble-of-averages | 23 | Ensemble of averages: Improving model selection and boosting performance in domain generalization | https://scholar.google.com/scholar?cluster=15173888902899249726&hl=en&as_sdt=0,14 | 4 | 2,022 |
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward | 1 | neurips | 4 | 2 | 2023-06-16 22:57:46.929000 | https://github.com/stanfordvl/alignment | 15 | ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward | https://scholar.google.com/scholar?cluster=8301128745364008098&hl=en&as_sdt=0,33 | 13 | 2,022 |
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption | 3 | neurips | 1 | 0 | 2023-06-16 22:57:47.140000 | https://github.com/graphpku/open-world-kg | 15 | Rethinking Knowledge Graph Evaluation Under the Open-World Assumption | https://scholar.google.com/scholar?cluster=12035243594832230326&hl=en&as_sdt=0,5 | 1 | 2,022 |
Neural Basis Models for Interpretability | 10 | neurips | 11 | 2 | 2023-06-16 22:57:47.351000 | https://github.com/facebookresearch/nbm-spam | 67 | Neural basis models for interpretability | https://scholar.google.com/scholar?cluster=7073329211572606092&hl=en&as_sdt=0,43 | 7 | 2,022 |
RecursiveMix: Mixed Learning with History | 9 | neurips | 0 | 0 | 2023-06-16 22:57:47.562000 | https://github.com/implus/RecursiveMix-pytorch | 20 | Recursivemix: Mixed learning with history | https://scholar.google.com/scholar?cluster=6486900347398545273&hl=en&as_sdt=0,5 | 2 | 2,022 |
Truly Deterministic Policy Optimization | 0 | neurips | 1 | 0 | 2023-06-16 22:57:47.774000 | https://github.com/ehsansaleh/code_tdpo | 6 | Truly Deterministic Policy Optimization | https://scholar.google.com/scholar?cluster=11328055735791293135&hl=en&as_sdt=0,5 | 1 | 2,022 |
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners | 21 | neurips | 1 | 1 | 2023-06-16 22:57:47.986000 | https://github.com/mikewangwzhl/vidil | 86 | Language models with image descriptors are strong few-shot video-language learners | https://scholar.google.com/scholar?cluster=15080693781137869549&hl=en&as_sdt=0,5 | 5 | 2,022 |
3DB: A Framework for Debugging Computer Vision Models | 33 | neurips | 4 | 3 | 2023-06-16 22:57:48.199000 | https://github.com/3db/3db | 119 | 3db: A framework for debugging computer vision models | https://scholar.google.com/scholar?cluster=8728632579792166672&hl=en&as_sdt=0,5 | 2 | 2,022 |
Formulating Robustness Against Unforeseen Attacks | 0 | neurips | 1 | 0 | 2023-06-16 22:57:48.414000 | https://github.com/inspire-group/variation-regularization | 5 | Formulating Robustness Against Unforeseen Attacks | https://scholar.google.com/scholar?cluster=5421072397038680742&hl=en&as_sdt=0,40 | 2 | 2,022 |
Single Model Uncertainty Estimation via Stochastic Data Centering | 5 | neurips | 0 | 0 | 2023-06-16 22:57:48.625000 | https://github.com/llnl/deltauq | 7 | Single model uncertainty estimation via stochastic data centering | https://scholar.google.com/scholar?cluster=2306475952584377994&hl=en&as_sdt=0,39 | 7 | 2,022 |
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries | 0 | neurips | 0 | 1 | 2023-06-16 22:57:48.837000 | https://github.com/atomwiseinc/cslvae | 9 | An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries | https://scholar.google.com/scholar?cluster=11892068807664304889&hl=en&as_sdt=0,5 | 5 | 2,022 |
Learning to Discover and Detect Objects | 0 | neurips | 6 | 3 | 2023-06-16 22:57:49.049000 | https://github.com/vlfom/rncdl | 103 | Learning to Discover and Detect Objects | https://scholar.google.com/scholar?cluster=11909305933195951417&hl=en&as_sdt=0,10 | 6 | 2,022 |
Simulation-guided Beam Search for Neural Combinatorial Optimization | 3 | neurips | 3 | 0 | 2023-06-16 22:57:49.260000 | https://github.com/yd-kwon/sgbs | 14 | Simulation-guided beam search for neural combinatorial optimization | https://scholar.google.com/scholar?cluster=8865912688547118342&hl=en&as_sdt=0,5 | 2 | 2,022 |
VICRegL: Self-Supervised Learning of Local Visual Features | 22 | neurips | 23 | 4 | 2023-06-16 22:57:49.471000 | https://github.com/facebookresearch/vicregl | 207 | Vicregl: Self-supervised learning of local visual features | https://scholar.google.com/scholar?cluster=11133634648290997125&hl=en&as_sdt=0,5 | 3 | 2,022 |
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC | 3 | neurips | 0 | 0 | 2023-06-16 22:57:49.682000 | https://github.com/akuzina/defend_vae_mcmc | 8 | Alleviating adversarial attacks on variational autoencoders with mcmc | https://scholar.google.com/scholar?cluster=8237174979788219482&hl=en&as_sdt=0,33 | 1 | 2,022 |
Human-AI Shared Control via Policy Dissection | 1 | neurips | 18 | 2 | 2023-06-16 22:57:49.894000 | https://github.com/mehooz/vision4leg | 147 | Human-AI Shared Control via Policy Dissection | https://scholar.google.com/scholar?cluster=17744727893155269891&hl=en&as_sdt=0,32 | 3 | 2,022 |
ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model | 16 | neurips | 0 | 1 | 2023-06-16 22:57:50.105000 | https://github.com/FreddieRao/ShapeCrafter | 14 | Shapecrafter: A recursive text-conditioned 3d shape generation model | https://scholar.google.com/scholar?cluster=1052962092907886930&hl=en&as_sdt=0,21 | 4 | 2,022 |
GraB: Finding Provably Better Data Permutations than Random Reshuffling | 6 | neurips | 1 | 0 | 2023-06-16 22:57:50.332000 | https://github.com/eugenelyc/grab | 2 | GraB: Finding Provably Better Data Permutations than Random Reshuffling | https://scholar.google.com/scholar?cluster=3880285491961366198&hl=en&as_sdt=0,44 | 2 | 2,022 |
Neural Stochastic Control | 6 | neurips | 0 | 0 | 2023-06-16 22:57:50.543000 | https://github.com/jingddong-zhang/neural-stochastic-control | 1 | Neural Stochastic Control | https://scholar.google.com/scholar?cluster=14553634387997941759&hl=en&as_sdt=0,5 | 1 | 2,022 |
Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales | 0 | neurips | 0 | 0 | 2023-06-16 22:57:50.770000 | https://github.com/tliu1997/ti-svm | 3 | Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales | https://scholar.google.com/scholar?cluster=10710745064843707287&hl=en&as_sdt=0,10 | 1 | 2,022 |
Equivariant Graph Hierarchy-Based Neural Networks | 4 | neurips | 2 | 0 | 2023-06-16 22:57:50.981000 | https://github.com/hanjq17/eghn | 13 | Equivariant graph hierarchy-based neural networks | https://scholar.google.com/scholar?cluster=18252825735214401175&hl=en&as_sdt=0,5 | 3 | 2,022 |
Learning interacting dynamical systems with latent Gaussian process ODEs | 0 | neurips | 0 | 0 | 2023-06-16 22:57:51.193000 | https://github.com/boschresearch/igpode | 3 | Learning interacting dynamical systems with latent Gaussian process ODEs | https://scholar.google.com/scholar?cluster=12254489423226434147&hl=en&as_sdt=0,37 | 5 | 2,022 |
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics | 4 | neurips | 0 | 0 | 2023-06-16 22:57:51.417000 | https://github.com/olivesgatech/olives_dataset | 2 | Olives dataset: Ophthalmic labels for investigating visual eye semantics | https://scholar.google.com/scholar?cluster=15665408901365199710&hl=en&as_sdt=0,5 | 5 | 2,022 |
Off-Policy Evaluation for Action-Dependent Non-stationary Environments | 1 | neurips | 0 | 0 | 2023-06-16 22:57:51.627000 | https://github.com/yashchandak/activens | 0 | Off-policy evaluation for action-dependent non-stationary environments | https://scholar.google.com/scholar?cluster=10431719067625816055&hl=en&as_sdt=0,25 | 2 | 2,022 |
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning | 12 | neurips | 126 | 26 | 2023-06-16 22:57:51.839000 | https://github.com/alibaba/federatedscope | 956 | pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning | https://scholar.google.com/scholar?cluster=18376990207026660571&hl=en&as_sdt=0,48 | 14 | 2,022 |
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition | 18 | neurips | 17 | 2 | 2023-06-16 22:57:52.051000 | https://github.com/kssteven418/squeezeformer | 191 | Squeezeformer: An efficient transformer for automatic speech recognition | https://scholar.google.com/scholar?cluster=8988041508983958224&hl=en&as_sdt=0,45 | 14 | 2,022 |
Deep Generalized Schrödinger Bridge | 8 | neurips | 1 | 0 | 2023-06-16 22:57:52.262000 | https://github.com/ghliu/deepgsb | 36 | Deep Generalized Schr\" odinger Bridge | https://scholar.google.com/scholar?cluster=6936079050426001825&hl=en&as_sdt=0,7 | 2 | 2,022 |
Learning sparse features can lead to overfitting in neural networks | 3 | neurips | 0 | 0 | 2023-06-16 22:57:52.475000 | https://github.com/pcsl-epfl/regressionsphere | 3 | Learning sparse features can lead to overfitting in neural networks | https://scholar.google.com/scholar?cluster=8395151871691062338&hl=en&as_sdt=0,14 | 2 | 2,022 |
Learning Distinct and Representative Modes for Image Captioning | 3 | neurips | 0 | 2 | 2023-06-16 22:57:52.687000 | https://github.com/bladewaltz1/modecap | 20 | Learning Distinct and Representative Modes for Image Captioning | https://scholar.google.com/scholar?cluster=10888606721940900950&hl=en&as_sdt=0,5 | 2 | 2,022 |
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics | 32 | neurips | 14 | 2 | 2023-06-16 22:57:52.898000 | https://github.com/qkaren/cold_decoding | 75 | Cold decoding: Energy-based constrained text generation with langevin dynamics | https://scholar.google.com/scholar?cluster=12031688945546236055&hl=en&as_sdt=0,33 | 5 | 2,022 |
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution | 8 | neurips | 0 | 0 | 2023-06-16 22:57:53.108000 | https://github.com/xyvivian/robod | 3 | Hyperparameter sensitivity in deep outlier detection: Analysis and a scalable hyper-ensemble solution | https://scholar.google.com/scholar?cluster=14214777377381746715&hl=en&as_sdt=0,41 | 1 | 2,022 |
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions | 1 | neurips | 1 | 0 | 2023-06-16 22:57:53.320000 | https://github.com/aria-systems-group/neuralnetcontrolbarrier | 4 | Safety guarantees for neural network dynamic systems via stochastic barrier functions | https://scholar.google.com/scholar?cluster=18263541328322655403&hl=en&as_sdt=0,33 | 1 | 2,022 |
On Margins and Generalisation for Voting Classifiers | 4 | neurips | 0 | 0 | 2023-06-16 22:57:53.531000 | https://github.com/vzantedeschi/dirichlet-margin-bound | 0 | On margins and generalisation for voting classifiers | https://scholar.google.com/scholar?cluster=12765469893892514877&hl=en&as_sdt=0,5 | 1 | 2,022 |
Rethinking the Reverse-engineering of Trojan Triggers | 1 | neurips | 2 | 0 | 2023-06-16 22:57:53.742000 | https://github.com/ru-system-software-and-security/featurere | 12 | Rethinking the Reverse-engineering of Trojan Triggers | https://scholar.google.com/scholar?cluster=17539542989635625416&hl=en&as_sdt=0,5 | 1 | 2,022 |
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling | 0 | neurips | 8 | 4 | 2023-06-16 22:57:53.953000 | https://github.com/neuralchen/RainNet | 31 | RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling | https://scholar.google.com/scholar?cluster=2526557995454698490&hl=en&as_sdt=0,5 | 1 | 2,022 |
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