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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time | 9 | neurips | 2 | 1 | 2023-06-16 16:05:32.717000 | https://github.com/snel-repo/sbtt-demo | 6 | Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time | https://scholar.google.com/scholar?cluster=116915416169272448&hl=en&as_sdt=0,33 | 3 | 2,021 |
Preserved central model for faster bidirectional compression in distributed settings | 22 | neurips | 0 | 0 | 2023-06-16 16:05:32.918000 | https://github.com/philipco/mcm-bidirectional-compression | 1 | Preserved central model for faster bidirectional compression in distributed settings | https://scholar.google.com/scholar?cluster=11324851301084839987&hl=en&as_sdt=0,5 | 1 | 2,021 |
Luna: Linear Unified Nested Attention | 64 | neurips | 15 | 1 | 2023-06-16 16:05:33.123000 | https://github.com/XuezheMax/fairseq-apollo | 94 | Luna: Linear unified nested attention | https://scholar.google.com/scholar?cluster=15945065740745831634&hl=en&as_sdt=0,33 | 6 | 2,021 |
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias | 6 | neurips | 9 | 0 | 2023-06-16 16:05:33.323000 | https://github.com/IntelLabs/causality-lab | 53 | Iterative causal discovery in the possible presence of latent confounders and selection bias | https://scholar.google.com/scholar?cluster=15917731379630778872&hl=en&as_sdt=0,38 | 10 | 2,021 |
Associating Objects with Transformers for Video Object Segmentation | 96 | neurips | 6 | 0 | 2023-06-16 16:05:33.522000 | https://github.com/z-x-yang/AOT | 91 | Associating objects with transformers for video object segmentation | https://scholar.google.com/scholar?cluster=3585510538357549856&hl=en&as_sdt=0,21 | 13 | 2,021 |
Automatic Symmetry Discovery with Lie Algebra Convolutional Network | 42 | neurips | 3 | 0 | 2023-06-16 16:05:33.720000 | https://github.com/nimadehmamy/l-conv-code | 38 | Automatic symmetry discovery with lie algebra convolutional network | https://scholar.google.com/scholar?cluster=14029131064477993418&hl=en&as_sdt=0,5 | 4 | 2,021 |
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks | 14 | neurips | 4 | 0 | 2023-06-16 16:05:33.920000 | https://github.com/gmum/Zero-Time-Waste | 12 | Zero time waste: recycling predictions in early exit neural networks | https://scholar.google.com/scholar?cluster=16788123232185667658&hl=en&as_sdt=0,44 | 5 | 2,021 |
On Model Calibration for Long-Tailed Object Detection and Instance Segmentation | 26 | neurips | 2 | 1 | 2023-06-16 16:05:34.120000 | https://github.com/tydpan/NorCal | 27 | On model calibration for long-tailed object detection and instance segmentation | https://scholar.google.com/scholar?cluster=2480452967273135514&hl=en&as_sdt=0,11 | 2 | 2,021 |
ReSSL: Relational Self-Supervised Learning with Weak Augmentation | 50 | neurips | 8 | 1 | 2023-06-16 16:05:34.327000 | https://github.com/KyleZheng1997/ReSSL | 50 | Ressl: Relational self-supervised learning with weak augmentation | https://scholar.google.com/scholar?cluster=9030640366859568915&hl=en&as_sdt=0,10 | 3 | 2,021 |
Learning to See by Looking at Noise | 14 | neurips | 5 | 2 | 2023-06-16 16:05:34.527000 | https://github.com/mbaradad/learning_with_noise | 91 | Learning to see by looking at noise | https://scholar.google.com/scholar?cluster=17950334231348284249&hl=en&as_sdt=0,33 | 5 | 2,021 |
Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN | 14 | neurips | 21 | 13 | 2023-06-16 16:05:34.726000 | https://github.com/xiezhy6/pasta-gan | 76 | Towards scalable unpaired virtual try-on via patch-routed spatially-adaptive GAN | https://scholar.google.com/scholar?cluster=9712953587399366251&hl=en&as_sdt=0,34 | 3 | 2,021 |
Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models | 52 | neurips | 2 | 0 | 2023-06-16 16:05:34.926000 | https://github.com/oxai/intersectional_gpt2 | 9 | Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models | https://scholar.google.com/scholar?cluster=10610853007934037556&hl=en&as_sdt=0,10 | 8 | 2,021 |
Weisfeiler and Lehman Go Cellular: CW Networks | 121 | neurips | 20 | 0 | 2023-06-16 16:05:35.127000 | https://github.com/twitter-research/cwn | 124 | Weisfeiler and lehman go cellular: Cw networks | https://scholar.google.com/scholar?cluster=10604779220263542295&hl=en&as_sdt=0,33 | 7 | 2,021 |
Learning Conjoint Attentions for Graph Neural Nets | 15 | neurips | 0 | 0 | 2023-06-16 16:05:35.328000 | https://github.com/he-tiantian/cats | 5 | Learning conjoint attentions for graph neural nets | https://scholar.google.com/scholar?cluster=4054823873527255592&hl=en&as_sdt=0,18 | 2 | 2,021 |
Aligned Structured Sparsity Learning for Efficient Image Super-Resolution | 29 | neurips | 8 | 1 | 2023-06-16 16:05:35.527000 | https://github.com/mingsun-tse/assl | 53 | Aligned structured sparsity learning for efficient image super-resolution | https://scholar.google.com/scholar?cluster=11894104122584992183&hl=en&as_sdt=0,5 | 8 | 2,021 |
Lip to Speech Synthesis with Visual Context Attentional GAN | 22 | neurips | 4 | 0 | 2023-06-16 16:05:35.726000 | https://github.com/ms-dot-k/Visual-Context-Attentional-GAN | 12 | Lip to speech synthesis with visual context attentional GAN | https://scholar.google.com/scholar?cluster=3002779332675732669&hl=en&as_sdt=0,5 | 1 | 2,021 |
Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning | 6 | neurips | 1 | 0 | 2023-06-16 16:05:35.927000 | https://github.com/kibeomkim/gace-gdan | 24 | Goal-aware cross-entropy for multi-target reinforcement learning | https://scholar.google.com/scholar?cluster=16382968152534550618&hl=en&as_sdt=0,16 | 3 | 2,021 |
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images | 52 | neurips | 11 | 1 | 2023-06-16 16:05:36.129000 | https://github.com/taconite/MetaAvatar-release | 109 | Metaavatar: Learning animatable clothed human models from few depth images | https://scholar.google.com/scholar?cluster=16058062617951022189&hl=en&as_sdt=0,33 | 6 | 2,021 |
Distributed Principal Component Analysis with Limited Communication | 5 | neurips | 0 | 0 | 2023-06-16 16:05:36.329000 | https://github.com/ist-daslab/qrgd | 2 | Distributed principal component analysis with limited communication | https://scholar.google.com/scholar?cluster=16491167680974044307&hl=en&as_sdt=0,5 | 4 | 2,021 |
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update | 13 | neurips | 4 | 2 | 2023-06-16 16:05:36.529000 | https://github.com/lessketching/newtonsketch | 1 | Newton-LESS: Sparsification without trade-offs for the sketched newton update | https://scholar.google.com/scholar?cluster=8971361646067584316&hl=en&as_sdt=0,33 | 1 | 2,021 |
Confident Anchor-Induced Multi-Source Free Domain Adaptation | 34 | neurips | 1 | 1 | 2023-06-16 16:05:36.733000 | https://github.com/learning-group123/caida | 18 | Confident anchor-induced multi-source free domain adaptation | https://scholar.google.com/scholar?cluster=4891466716654628888&hl=en&as_sdt=0,26 | 2 | 2,021 |
Word2Fun: Modelling Words as Functions for Diachronic Word Representation | 1 | neurips | 0 | 1 | 2023-06-16 16:05:36.936000 | https://github.com/wabyking/word2fun | 10 | Word2Fun: Modelling Words as Functions for Diachronic Word Representation | https://scholar.google.com/scholar?cluster=14848701185772884980&hl=en&as_sdt=0,33 | 1 | 2,021 |
Low-Rank Constraints for Fast Inference in Structured Models | 10 | neurips | 0 | 1 | 2023-06-16 16:05:37.139000 | https://github.com/justinchiu/low-rank-models | 5 | Low-rank constraints for fast inference in structured models | https://scholar.google.com/scholar?cluster=15216352374611711176&hl=en&as_sdt=0,14 | 3 | 2,021 |
Accumulative Poisoning Attacks on Real-time Data | 11 | neurips | 1 | 0 | 2023-06-16 16:05:37.341000 | https://github.com/ShawnXYang/AccumulativeAttack | 17 | Accumulative poisoning attacks on real-time data | https://scholar.google.com/scholar?cluster=17018461129104727462&hl=en&as_sdt=0,44 | 2 | 2,021 |
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators | 28 | neurips | 5 | 1 | 2023-06-16 16:05:37.541000 | https://github.com/ai-secure/g-pate | 23 | G-PATE: scalable differentially private data generator via private aggregation of teacher discriminators | https://scholar.google.com/scholar?cluster=18094495377911036601&hl=en&as_sdt=0,5 | 2 | 2,021 |
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning | 8 | neurips | 3 | 1 | 2023-06-16 16:05:37.747000 | https://github.com/alinlab/oreo | 21 | Object-aware regularization for addressing causal confusion in imitation learning | https://scholar.google.com/scholar?cluster=11591778827238296891&hl=en&as_sdt=0,5 | 3 | 2,021 |
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks | 37 | neurips | 0 | 0 | 2023-06-16 16:05:37.948000 | https://github.com/cog-imperial/partitionedformulations_nn | 2 | Partition-based formulations for mixed-integer optimization of trained relu neural networks | https://scholar.google.com/scholar?cluster=322600744726062077&hl=en&as_sdt=0,44 | 3 | 2,021 |
Hyperparameter Optimization Is Deceiving Us, and How to Stop It | 12 | neurips | 1 | 0 | 2023-06-16 16:05:38.147000 | https://github.com/pasta41/deception | 0 | Hyperparameter optimization is deceiving us, and how to stop it | https://scholar.google.com/scholar?cluster=13676283395211391710&hl=en&as_sdt=0,14 | 3 | 2,021 |
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning | 8 | neurips | 1 | 0 | 2023-06-16 16:05:38.347000 | https://github.com/kristian-georgiev/sgmrl | 4 | On the convergence theory of debiased model-agnostic meta-reinforcement learning | https://scholar.google.com/scholar?cluster=4479200688561137043&hl=en&as_sdt=0,6 | 2 | 2,021 |
3D Pose Transfer with Correspondence Learning and Mesh Refinement | 12 | neurips | 4 | 0 | 2023-06-16 16:05:38.547000 | https://github.com/chaoyuesong/3d-corenet | 28 | 3D pose transfer with correspondence learning and mesh refinement | https://scholar.google.com/scholar?cluster=16594397098263890471&hl=en&as_sdt=0,5 | 7 | 2,021 |
Framing RNN as a kernel method: A neural ODE approach | 13 | neurips | 3 | 0 | 2023-06-16 16:05:38.746000 | https://github.com/afermanian/rnn-kernel | 6 | Framing RNN as a kernel method: A neural ODE approach | https://scholar.google.com/scholar?cluster=12320309652310006031&hl=en&as_sdt=0,33 | 3 | 2,021 |
Contextual Similarity Aggregation with Self-attention for Visual Re-ranking | 8 | neurips | 2 | 2 | 2023-06-16 16:05:38.947000 | https://github.com/mcc-wh/csa | 21 | Contextual similarity aggregation with self-attention for visual re-ranking | https://scholar.google.com/scholar?cluster=1731686966736408676&hl=en&as_sdt=0,1 | 2 | 2,021 |
Can Information Flows Suggest Targets for Interventions in Neural Circuits? | 2 | neurips | 1 | 0 | 2023-06-16 16:05:39.149000 | https://github.com/praveenv253/ann-info-flow | 0 | Can information flows suggest targets for interventions in neural circuits? | https://scholar.google.com/scholar?cluster=59078764435359692&hl=en&as_sdt=0,5 | 2 | 2,021 |
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes | 17 | neurips | 4 | 0 | 2023-06-16 16:05:39.348000 | https://github.com/zhaozhiqian/synctwin-neurips-2021 | 5 | Synctwin: Treatment effect estimation with longitudinal outcomes | https://scholar.google.com/scholar?cluster=12038492275286203225&hl=en&as_sdt=0,44 | 4 | 2,021 |
Unsupervised Motion Representation Learning with Capsule Autoencoders | 12 | neurips | 0 | 0 | 2023-06-16 16:05:39.548000 | https://github.com/ZiweiXU/CapsuleMotion | 9 | Unsupervised motion representation learning with capsule autoencoders | https://scholar.google.com/scholar?cluster=14303399087955819091&hl=en&as_sdt=0,5 | 1 | 2,021 |
Exploring Forensic Dental Identification with Deep Learning | 4 | neurips | 1 | 1 | 2023-06-16 16:05:39.749000 | https://github.com/liangyuandg/foid | 4 | Exploring forensic dental identification with deep learning | https://scholar.google.com/scholar?cluster=10294284615303387635&hl=en&as_sdt=0,5 | 1 | 2,021 |
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks | 49 | neurips | 36 | 0 | 2023-06-16 16:05:39.948000 | https://github.com/Future-Power-Networks/MAPDN | 98 | Multi-agent reinforcement learning for active voltage control on power distribution networks | https://scholar.google.com/scholar?cluster=339266555786095875&hl=en&as_sdt=0,10 | 2 | 2,021 |
Dangers of Bayesian Model Averaging under Covariate Shift | 26 | neurips | 2 | 0 | 2023-06-16 16:05:40.148000 | https://github.com/izmailovpavel/bnn_covariate_shift | 28 | Dangers of bayesian model averaging under covariate shift | https://scholar.google.com/scholar?cluster=9253304407956386101&hl=en&as_sdt=0,5 | 3 | 2,021 |
Towards Lower Bounds on the Depth of ReLU Neural Networks | 13 | neurips | 0 | 0 | 2023-06-16 16:05:40.347000 | https://github.com/ChristophHertrich/relu-mip-depth-bound | 0 | Towards lower bounds on the depth of ReLU neural networks | https://scholar.google.com/scholar?cluster=4120327399657306898&hl=en&as_sdt=0,5 | 1 | 2,021 |
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective | 13 | neurips | 0 | 0 | 2023-06-16 16:05:40.548000 | https://github.com/gpleiss/limits_of_large_width | 5 | The limitations of large width in neural networks: A deep Gaussian process perspective | https://scholar.google.com/scholar?cluster=18411382208005468775&hl=en&as_sdt=0,47 | 1 | 2,021 |
Exact marginal prior distributions of finite Bayesian neural networks | 24 | neurips | 0 | 0 | 2023-06-16 16:05:40.751000 | https://github.com/pehlevan-group/exactbayesiannetworkpriors | 0 | Exact marginal prior distributions of finite Bayesian neural networks | https://scholar.google.com/scholar?cluster=8265985358387037900&hl=en&as_sdt=0,5 | 2 | 2,021 |
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees | 4 | neurips | 1 | 0 | 2023-06-16 16:05:40.978000 | https://github.com/kjason/resnest | 2 | ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees | https://scholar.google.com/scholar?cluster=9550021969422604022&hl=en&as_sdt=0,5 | 1 | 2,021 |
Repulsive Deep Ensembles are Bayesian | 44 | neurips | 5 | 0 | 2023-06-16 16:05:41.182000 | https://github.com/ratschlab/repulsive_ensembles | 13 | Repulsive deep ensembles are bayesian | https://scholar.google.com/scholar?cluster=4880325796914110864&hl=en&as_sdt=0,5 | 5 | 2,021 |
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM) | 2 | neurips | 4 | 0 | 2023-06-16 16:05:41.383000 | https://github.com/jayroxis/dam-pytorch | 15 | Learning compact representations of neural networks using discriminative masking (dam) | https://scholar.google.com/scholar?cluster=14512990192508822553&hl=en&as_sdt=0,44 | 2 | 2,021 |
Neural Auto-Curricula in Two-Player Zero-Sum Games | 20 | neurips | 3 | 0 | 2023-06-16 16:05:41.584000 | https://github.com/waterhorse1/nac | 21 | Neural auto-curricula in two-player zero-sum games | https://scholar.google.com/scholar?cluster=9201661815839550883&hl=en&as_sdt=0,5 | 2 | 2,021 |
From global to local MDI variable importances for random forests and when they are Shapley values | 7 | neurips | 0 | 0 | 2023-06-16 16:05:41.792000 | https://github.com/asutera/local-mdi-importance | 3 | From global to local MDI variable importances for random forests and when they are Shapley values | https://scholar.google.com/scholar?cluster=18342054531367511892&hl=en&as_sdt=0,5 | 1 | 2,021 |
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness | 41 | neurips | 11 | 0 | 2023-06-16 16:05:42.038000 | https://github.com/facebookresearch/augmentation-corruption | 38 | On interaction between augmentations and corruptions in natural corruption robustness | https://scholar.google.com/scholar?cluster=440630592288573899&hl=en&as_sdt=0,10 | 7 | 2,021 |
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data | 37 | neurips | 5 | 2 | 2023-06-16 16:05:42.239000 | https://github.com/asrafulashiq/dynamic-cdfsl | 25 | Dynamic distillation network for cross-domain few-shot recognition with unlabeled data | https://scholar.google.com/scholar?cluster=9716577277370774605&hl=en&as_sdt=0,5 | 4 | 2,021 |
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations | 29 | neurips | 1 | 1 | 2023-06-16 16:05:42.450000 | https://github.com/peterbhase/ExplanationSearch | 15 | The out-of-distribution problem in explainability and search methods for feature importance explanations | https://scholar.google.com/scholar?cluster=11979193341973776256&hl=en&as_sdt=0,5 | 1 | 2,021 |
Control Variates for Slate Off-Policy Evaluation | 3 | neurips | 0 | 0 | 2023-06-16 16:05:42.653000 | https://github.com/fernandoamat/slateope | 3 | Control variates for slate off-policy evaluation | https://scholar.google.com/scholar?cluster=7057011324301771972&hl=en&as_sdt=0,5 | 1 | 2,021 |
Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation | 50 | neurips | 32 | 3 | 2023-06-16 16:05:42.856000 | https://github.com/nicklashansen/dmcontrol-generalization-benchmark | 121 | Stabilizing deep q-learning with convnets and vision transformers under data augmentation | https://scholar.google.com/scholar?cluster=6794503273897899990&hl=en&as_sdt=0,26 | 4 | 2,021 |
On Effective Scheduling of Model-based Reinforcement Learning | 6 | neurips | 0 | 0 | 2023-06-16 16:05:43.056000 | https://github.com/hanglai/autombpo | 11 | On effective scheduling of model-based reinforcement learning | https://scholar.google.com/scholar?cluster=11128521607771619105&hl=en&as_sdt=0,5 | 1 | 2,021 |
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience | 5 | neurips | 1 | 0 | 2023-06-16 16:05:43.259000 | https://github.com/eulerlab/rave | 8 | Removing inter-experimental variability from functional data in systems neuroscience | https://scholar.google.com/scholar?cluster=6596108345516212065&hl=en&as_sdt=0,5 | 3 | 2,021 |
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components | 3 | neurips | 1 | 0 | 2023-06-16 16:05:43.459000 | https://github.com/nveldt/SparseCardDSFM | 1 | Approximate decomposable submodular function minimization for cardinality-based components | https://scholar.google.com/scholar?cluster=7765734626612115875&hl=en&as_sdt=0,5 | 2 | 2,021 |
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution | 5 | neurips | 0 | 1 | 2023-06-16 16:05:43.659000 | https://github.com/ars22/meta-learning-eval-id-vs-ood | 1 | Two sides of meta-learning evaluation: In vs. out of distribution | https://scholar.google.com/scholar?cluster=3248310209715009715&hl=en&as_sdt=0,5 | 3 | 2,021 |
Debiased Visual Question Answering from Feature and Sample Perspectives | 19 | neurips | 7 | 7 | 2023-06-16 16:05:43.860000 | https://github.com/zhiquan-wen/d-vqa | 20 | Debiased visual question answering from feature and sample perspectives | https://scholar.google.com/scholar?cluster=9092713122749845551&hl=en&as_sdt=0,5 | 0 | 2,021 |
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness | 7 | neurips | 3 | 2 | 2023-06-16 16:05:44.060000 | https://github.com/jie-ren/a-unified-game-theoretic-interpretation-of-adversarial-robustness | 18 | Towards a unified game-theoretic view of adversarial perturbations and robustness | https://scholar.google.com/scholar?cluster=10405183538906234310&hl=en&as_sdt=0,5 | 1 | 2,021 |
On the Out-of-distribution Generalization of Probabilistic Image Modelling | 21 | neurips | 0 | 0 | 2023-06-16 16:05:44.260000 | https://github.com/zmtomorrow/nelloc | 9 | On the out-of-distribution generalization of probabilistic image modelling | https://scholar.google.com/scholar?cluster=16600938628354788442&hl=en&as_sdt=0,5 | 1 | 2,021 |
Information Directed Reward Learning for Reinforcement Learning | 6 | neurips | 1 | 0 | 2023-06-16 16:05:44.460000 | https://github.com/david-lindner/idrl | 9 | Information directed reward learning for reinforcement learning | https://scholar.google.com/scholar?cluster=8772252576862267451&hl=en&as_sdt=0,47 | 4 | 2,021 |
SSMF: Shifting Seasonal Matrix Factorization | 2 | neurips | 2 | 0 | 2023-06-16 16:05:44.668000 | https://github.com/kokikwbt/ssmf | 10 | Ssmf: Shifting seasonal matrix factorization | https://scholar.google.com/scholar?cluster=11697569962161025412&hl=en&as_sdt=0,6 | 1 | 2,021 |
Robust and differentially private mean estimation | 40 | neurips | 0 | 0 | 2023-06-16 16:05:44.871000 | https://github.com/xiyangl3/robust_dp | 3 | Robust and differentially private mean estimation | https://scholar.google.com/scholar?cluster=4295339113216361062&hl=en&as_sdt=0,5 | 2 | 2,021 |
Adaptable Agent Populations via a Generative Model of Policies | 8 | neurips | 0 | 2 | 2023-06-16 16:05:45.071000 | https://github.com/kennyderek/adap | 11 | Adaptable agent populations via a generative model of policies | https://scholar.google.com/scholar?cluster=11064961923408119459&hl=en&as_sdt=0,5 | 2 | 2,021 |
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity | 11 | neurips | 3 | 3 | 2023-06-16 16:05:45.272000 | https://github.com/bcmi/tramas-weak-shot-object-detection | 50 | Mixed supervised object detection by transferring mask prior and semantic similarity | https://scholar.google.com/scholar?cluster=809819108668093612&hl=en&as_sdt=0,5 | 7 | 2,021 |
IQ-Learn: Inverse soft-Q Learning for Imitation | 43 | neurips | 26 | 5 | 2023-06-16 16:05:45.471000 | https://github.com/Div99/IQ-Learn | 135 | Iq-learn: Inverse soft-q learning for imitation | https://scholar.google.com/scholar?cluster=267480393884738505&hl=en&as_sdt=0,10 | 2 | 2,021 |
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data | 6 | neurips | 2 | 0 | 2023-06-16 16:05:45.672000 | https://github.com/kaist-dmlab/taufe | 7 | Task-agnostic undesirable feature deactivation using out-of-distribution data | https://scholar.google.com/scholar?cluster=15884866726240245144&hl=en&as_sdt=0,21 | 2 | 2,021 |
Speedy Performance Estimation for Neural Architecture Search | 19 | neurips | 0 | 1 | 2023-06-16 16:05:45.872000 | https://github.com/rubinxin/TSE | 8 | Speedy performance estimation for neural architecture search | https://scholar.google.com/scholar?cluster=12649354892939725087&hl=en&as_sdt=0,5 | 1 | 2,021 |
Environment Generation for Zero-Shot Compositional Reinforcement Learning | 19 | neurips | 7,321 | 1,026 | 2023-06-16 16:05:46.078000 | https://github.com/google-research/google-research | 29,786 | Environment generation for zero-shot compositional reinforcement learning | https://scholar.google.com/scholar?cluster=4049956378759656568&hl=en&as_sdt=0,5 | 727 | 2,021 |
Optimizing Conditional Value-At-Risk of Black-Box Functions | 10 | neurips | 2 | 0 | 2023-06-16 16:05:46.278000 | https://github.com/qphong/bayesopt-lv | 1 | Optimizing conditional value-at-risk of black-box functions | https://scholar.google.com/scholar?cluster=1243167075412658030&hl=en&as_sdt=0,5 | 1 | 2,021 |
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning | 22 | neurips | 7 | 1 | 2023-06-16 16:05:46.479000 | https://github.com/chongjiange/care | 116 | Revitalizing cnn attention via transformers in self-supervised visual representation learning | https://scholar.google.com/scholar?cluster=11137326961804977691&hl=en&as_sdt=0,10 | 6 | 2,021 |
Learning to Learn Graph Topologies | 15 | neurips | 3 | 0 | 2023-06-16 16:05:46.680000 | https://github.com/xpuoxford/l2g-neurips2021 | 18 | Learning to learn graph topologies | https://scholar.google.com/scholar?cluster=6887973786384581527&hl=en&as_sdt=0,5 | 2 | 2,021 |
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks | 18 | neurips | 9 | 0 | 2023-06-16 16:05:46.879000 | https://github.com/rainorangelemon/gnn-motion-planning | 61 | Reducing collision checking for sampling-based motion planning using graph neural networks | https://scholar.google.com/scholar?cluster=15148652525294899591&hl=en&as_sdt=0,5 | 5 | 2,021 |
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons | 1 | neurips | 0 | 0 | 2023-06-16 16:05:47.079000 | https://github.com/wenboren/multi-wise-ranking | 0 | Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons | https://scholar.google.com/scholar?cluster=2656146730518033072&hl=en&as_sdt=0,33 | 1 | 2,021 |
Efficient Bayesian network structure learning via local Markov boundary search | 7 | neurips | 0 | 0 | 2023-06-16 16:05:47.279000 | https://github.com/minggao97/tam | 0 | Efficient Bayesian network structure learning via local Markov boundary search | https://scholar.google.com/scholar?cluster=9088410418328444112&hl=en&as_sdt=0,5 | 2 | 2,021 |
Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention | 36 | neurips | 13 | 3 | 2023-06-16 16:05:47.479000 | https://github.com/egyptdj/stagin | 53 | Learning dynamic graph representation of brain connectome with spatio-temporal attention | https://scholar.google.com/scholar?cluster=4519412816058293652&hl=en&as_sdt=0,21 | 2 | 2,021 |
Understanding the Generalization Benefit of Model Invariance from a Data Perspective | 15 | neurips | 0 | 0 | 2023-06-16 16:05:47.679000 | https://github.com/bangann/understanding-invariance | 1 | Understanding the generalization benefit of model invariance from a data perspective | https://scholar.google.com/scholar?cluster=6413093922837759333&hl=en&as_sdt=0,29 | 2 | 2,021 |
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? | 20 | neurips | 2 | 1 | 2023-06-16 16:05:47.880000 | https://github.com/dongxinshuai/rift-neurips2021 | 10 | How should pre-trained language models be fine-tuned towards adversarial robustness? | https://scholar.google.com/scholar?cluster=6181501372653861648&hl=en&as_sdt=0,36 | 5 | 2,021 |
Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks | 3 | neurips | 2 | 0 | 2023-06-16 16:05:48.080000 | https://github.com/robert-lieck/rbn | 12 | Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks | https://scholar.google.com/scholar?cluster=7508062902599380178&hl=en&as_sdt=0,5 | 1 | 2,021 |
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration | 17 | neurips | 5 | 0 | 2023-06-16 16:05:48.280000 | https://github.com/gavinkerrigan/conf_matrix_and_calibration | 7 | Combining human predictions with model probabilities via confusion matrices and calibration | https://scholar.google.com/scholar?cluster=12718168671257506616&hl=en&as_sdt=0,5 | 2 | 2,021 |
Probabilistic Attention for Interactive Segmentation | 2 | neurips | 9 | 0 | 2023-06-16 16:05:48.481000 | https://github.com/apple/ml-probabilistic-attention | 21 | Probabilistic attention for interactive segmentation | https://scholar.google.com/scholar?cluster=6574265597018003751&hl=en&as_sdt=0,47 | 6 | 2,021 |
Pruning Randomly Initialized Neural Networks with Iterative Randomization | 14 | neurips | 2 | 0 | 2023-06-16 16:05:48.680000 | https://github.com/dchiji-ntt/iterand | 9 | Pruning randomly initialized neural networks with iterative randomization | https://scholar.google.com/scholar?cluster=11749710093845056800&hl=en&as_sdt=0,7 | 2 | 2,021 |
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization | 13 | neurips | 1 | 0 | 2023-06-16 16:05:48.880000 | https://github.com/baofff/stability_ho | 2 | Stability and generalization of bilevel programming in hyperparameter optimization | https://scholar.google.com/scholar?cluster=3805382994554865062&hl=en&as_sdt=0,5 | 1 | 2,021 |
Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies | 21 | neurips | 8 | 0 | 2023-06-16 16:05:49.080000 | https://github.com/Rondorf/BOReL | 20 | Offline Meta Reinforcement Learning--Identifiability Challenges and Effective Data Collection Strategies | https://scholar.google.com/scholar?cluster=3592419884384460621&hl=en&as_sdt=0,5 | 3 | 2,021 |
Flexible Option Learning | 11 | neurips | 0 | 0 | 2023-06-16 16:05:49.280000 | https://github.com/mklissa/moc | 7 | Flexible option learning | https://scholar.google.com/scholar?cluster=1622137245379658654&hl=en&as_sdt=0,15 | 2 | 2,021 |
Credit Assignment in Neural Networks through Deep Feedback Control | 16 | neurips | 1 | 0 | 2023-06-16 16:05:49.480000 | https://github.com/meulemansalex/deep_feedback_control | 6 | Credit assignment in neural networks through deep feedback control | https://scholar.google.com/scholar?cluster=10215619151456904513&hl=en&as_sdt=0,33 | 2 | 2,021 |
Neural Additive Models: Interpretable Machine Learning with Neural Nets | 245 | neurips | 6 | 0 | 2023-06-16 16:05:49.680000 | https://github.com/lemeln/nam | 14 | Neural additive models: Interpretable machine learning with neural nets | https://scholar.google.com/scholar?cluster=14127065231811177587&hl=en&as_sdt=0,18 | 0 | 2,021 |
Kernel Functional Optimisation | 4 | neurips | 0 | 1 | 2023-06-16 16:05:49.880000 | https://github.com/mailtoarunkumarav/kernelfunctionaloptimisation | 2 | Kernel functional optimisation | https://scholar.google.com/scholar?cluster=9446899252048844733&hl=en&as_sdt=0,13 | 1 | 2,021 |
Generalized Shape Metrics on Neural Representations | 22 | neurips | 8 | 2 | 2023-06-16 16:05:50.082000 | https://github.com/ahwillia/netrep | 83 | Generalized shape metrics on neural representations | https://scholar.google.com/scholar?cluster=3294259291908791528&hl=en&as_sdt=0,39 | 3 | 2,021 |
Towards Robust Bisimulation Metric Learning | 20 | neurips | 2 | 0 | 2023-06-16 16:05:50.282000 | https://github.com/metekemertas/RobustBisimulation | 6 | Towards robust bisimulation metric learning | https://scholar.google.com/scholar?cluster=167387616529603590&hl=en&as_sdt=0,5 | 2 | 2,021 |
Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning | 22 | neurips | 1 | 0 | 2023-06-16 16:05:50.482000 | https://github.com/EkdeepSLubana/BeyondBatchNorm | 16 | Beyond batchnorm: Towards a unified understanding of normalization in deep learning | https://scholar.google.com/scholar?cluster=2227521021573022102&hl=en&as_sdt=0,31 | 3 | 2,021 |
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training | 5 | neurips | 1 | 0 | 2023-06-16 16:05:50.683000 | https://github.com/npminh12/nn-clt | 0 | Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training | https://scholar.google.com/scholar?cluster=17789162731650605846&hl=en&as_sdt=0,36 | 2 | 2,021 |
Medical Dead-ends and Learning to Identify High-Risk States and Treatments | 21 | neurips | 15 | 0 | 2023-06-16 16:05:50.882000 | https://github.com/microsoft/med-deadend | 43 | Medical dead-ends and learning to identify high-risk states and treatments | https://scholar.google.com/scholar?cluster=7718917214677411862&hl=en&as_sdt=0,34 | 6 | 2,021 |
Batch Normalization Orthogonalizes Representations in Deep Random Networks | 20 | neurips | 1 | 0 | 2023-06-16 16:05:51.080000 | https://github.com/hadidaneshmand/batchnorm21 | 3 | Batch normalization orthogonalizes representations in deep random networks | https://scholar.google.com/scholar?cluster=8201984774954479451&hl=en&as_sdt=0,5 | 1 | 2,021 |
Support vector machines and linear regression coincide with very high-dimensional features | 15 | neurips | 0 | 0 | 2023-06-16 16:05:51.281000 | https://github.com/scO0rpion/SVM-Proliferation-NIPS2021 | 1 | Support vector machines and linear regression coincide with very high-dimensional features | https://scholar.google.com/scholar?cluster=9835458066237043881&hl=en&as_sdt=0,5 | 2 | 2,021 |
Offline RL Without Off-Policy Evaluation | 67 | neurips | 1 | 4 | 2023-06-16 16:05:51.481000 | https://github.com/davidbrandfonbrener/onestep-rl | 27 | Offline rl without off-policy evaluation | https://scholar.google.com/scholar?cluster=16078097822784982755&hl=en&as_sdt=0,47 | 2 | 2,021 |
Continuous vs. Discrete Optimization of Deep Neural Networks | 17 | neurips | 0 | 0 | 2023-06-16 16:05:51.680000 | https://github.com/elkabzo/cont_disc_opt_dnn | 0 | Continuous vs. discrete optimization of deep neural networks | https://scholar.google.com/scholar?cluster=6909198963909680227&hl=en&as_sdt=0,5 | 3 | 2,021 |
Can contrastive learning avoid shortcut solutions? | 60 | neurips | 2 | 1 | 2023-06-16 16:05:51.884000 | https://github.com/joshr17/IFM | 45 | Can contrastive learning avoid shortcut solutions? | https://scholar.google.com/scholar?cluster=705841367969128558&hl=en&as_sdt=0,5 | 1 | 2,021 |
Convex Polytope Trees | 1 | neurips | 1 | 0 | 2023-06-16 16:05:52.115000 | https://github.com/rezaarmand/Convex_Polytope_Trees | 2 | Convex Polytope Trees | https://scholar.google.com/scholar?cluster=2959989804162360758&hl=en&as_sdt=0,44 | 1 | 2,021 |
Noisy Recurrent Neural Networks | 27 | neurips | 1 | 1 | 2023-06-16 16:05:52.315000 | https://github.com/erichson/NoisyRNN | 1 | Noisy recurrent neural networks | https://scholar.google.com/scholar?cluster=6463637827089951262&hl=en&as_sdt=0,33 | 2 | 2,021 |
Matrix encoding networks for neural combinatorial optimization | 20 | neurips | 10 | 1 | 2023-06-16 16:05:52.515000 | https://github.com/yd-kwon/MatNet | 45 | Matrix encoding networks for neural combinatorial optimization | https://scholar.google.com/scholar?cluster=13176466295428561186&hl=en&as_sdt=0,33 | 4 | 2,021 |
Continuous Latent Process Flows | 8 | neurips | 5 | 0 | 2023-06-16 16:05:52.716000 | https://github.com/borealisai/continuous-latent-process-flows | 8 | Continuous latent process flows | https://scholar.google.com/scholar?cluster=10696451274107290963&hl=en&as_sdt=0,45 | 2 | 2,021 |
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