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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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