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Sequence-to-Sequence Learning with Latent Neural Grammars
| 19 |
neurips
| 3 | 0 |
2023-06-16 16:08:13.686000
|
https://github.com/yoonkim/neural-qcfg
| 43 |
Sequence-to-sequence learning with latent neural grammars
|
https://scholar.google.com/scholar?cluster=8101496336796731630&hl=en&as_sdt=0,44
| 5 | 2,021 |
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
| 19 |
neurips
| 1 | 1 |
2023-06-16 16:08:13.886000
|
https://github.com/gt-ripl/geometric-sensitivity-decomposition
| 18 |
A geometric perspective towards neural calibration via sensitivity decomposition
|
https://scholar.google.com/scholar?cluster=11287329988480930857&hl=en&as_sdt=0,33
| 2 | 2,021 |
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
| 51 |
neurips
| 8 | 3 |
2023-06-16 16:08:14.086000
|
https://github.com/thesalon/fast-dpsgd
| 55 |
Enabling fast differentially private sgd via just-in-time compilation and vectorization
|
https://scholar.google.com/scholar?cluster=3530716804480287020&hl=en&as_sdt=0,15
| 2 | 2,021 |
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
| 34 |
neurips
| 2 | 0 |
2023-06-16 16:08:14.288000
|
https://github.com/anguyen8/effectiveness-attribution-maps
| 16 |
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
|
https://scholar.google.com/scholar?cluster=1502626993942622867&hl=en&as_sdt=0,33
| 3 | 2,021 |
Coordinated Proximal Policy Optimization
| 13 |
neurips
| 1 | 0 |
2023-06-16 16:08:14.489000
|
https://github.com/ZifanWu/Coordinated-PPO
| 6 |
Coordinated proximal policy optimization
|
https://scholar.google.com/scholar?cluster=3968189013521929332&hl=en&as_sdt=0,47
| 0 | 2,021 |
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning
| 30 |
neurips
| 6 | 1 |
2023-06-16 16:08:14.690000
|
https://github.com/grayhong/bias-contrastive-learning
| 20 |
Unbiased classification through bias-contrastive and bias-balanced learning
|
https://scholar.google.com/scholar?cluster=9164048874502815433&hl=en&as_sdt=0,32
| 2 | 2,021 |
Pragmatic Image Compression for Human-in-the-Loop Decision-Making
| 10 |
neurips
| 1 | 0 |
2023-06-16 16:08:14.890000
|
https://github.com/rddy/pico
| 10 |
Pragmatic Image Compression for Human-in-the-Loop Decision-Making
|
https://scholar.google.com/scholar?cluster=14120252900286558336&hl=en&as_sdt=0,23
| 1 | 2,021 |
Generalized Linear Bandits with Local Differential Privacy
| 14 |
neurips
| 0 | 0 |
2023-06-16 16:08:15.091000
|
https://github.com/liangzp/LDP-Bandit
| 13 |
Generalized linear bandits with local differential privacy
|
https://scholar.google.com/scholar?cluster=10585991561945031003&hl=en&as_sdt=0,11
| 2 | 2,021 |
Characterizing possible failure modes in physics-informed neural networks
| 217 |
neurips
| 24 | 2 |
2023-06-16 16:08:15.291000
|
https://github.com/a1k12/characterizing-pinns-failure-modes
| 71 |
Characterizing possible failure modes in physics-informed neural networks
|
https://scholar.google.com/scholar?cluster=269500818750259409&hl=en&as_sdt=0,10
| 5 | 2,021 |
Artistic Style Transfer with Internal-external Learning and Contrastive Learning
| 47 |
neurips
| 5 | 2 |
2023-06-16 16:08:15.492000
|
https://github.com/halbertch/iecontraast
| 60 |
Artistic style transfer with internal-external learning and contrastive learning
|
https://scholar.google.com/scholar?cluster=17574032712333265817&hl=en&as_sdt=0,47
| 3 | 2,021 |
Fast Abductive Learning by Similarity-based Consistency Optimization
| 11 |
neurips
| 1 | 0 |
2023-06-16 16:08:15.694000
|
https://github.com/abductivelearning/ablsim
| 8 |
Fast abductive learning by similarity-based consistency optimization
|
https://scholar.google.com/scholar?cluster=8539963460239876225&hl=en&as_sdt=0,5
| 2 | 2,021 |
The Elastic Lottery Ticket Hypothesis
| 19 |
neurips
| 3 | 0 |
2023-06-16 16:08:15.894000
|
https://github.com/VITA-Group/ElasticLTH
| 10 |
The elastic lottery ticket hypothesis
|
https://scholar.google.com/scholar?cluster=16545358675895401857&hl=en&as_sdt=0,33
| 9 | 2,021 |
Joint Inference for Neural Network Depth and Dropout Regularization
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:08:16.095000
|
https://github.com/MahdiGilany/Depth_and_Dropout
| 2 |
Joint inference for neural network depth and dropout regularization
|
https://scholar.google.com/scholar?cluster=9001704603020268713&hl=en&as_sdt=0,33
| 1 | 2,021 |
Improving Deep Learning Interpretability by Saliency Guided Training
| 31 |
neurips
| 2 | 0 |
2023-06-16 16:08:16.296000
|
https://github.com/ayaabdelsalam91/saliency_guided_training
| 8 |
Improving deep learning interpretability by saliency guided training
|
https://scholar.google.com/scholar?cluster=17593389442039305805&hl=en&as_sdt=0,33
| 1 | 2,021 |
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
| 14 |
neurips
| 5 | 0 |
2023-06-16 16:08:16.496000
|
https://github.com/chl8856/survite
| 16 |
SurvITE: learning heterogeneous treatment effects from time-to-event data
|
https://scholar.google.com/scholar?cluster=3737018677370322471&hl=en&as_sdt=0,5
| 1 | 2,021 |
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
| 11 |
neurips
| 0 | 0 |
2023-06-16 16:08:16.697000
|
https://github.com/lili-chen/SEER
| 21 |
Improving computational efficiency in visual reinforcement learning via stored embeddings
|
https://scholar.google.com/scholar?cluster=3434130720798218429&hl=en&as_sdt=0,5
| 2 | 2,021 |
Learning Generalized Gumbel-max Causal Mechanisms
| 10 |
neurips
| 7,321 | 1,026 |
2023-06-16 16:08:16.898000
|
https://github.com/google-research/google-research
| 29,786 |
Learning generalized gumbel-max causal mechanisms
|
https://scholar.google.com/scholar?cluster=5199832091407110116&hl=en&as_sdt=0,36
| 727 | 2,021 |
Are Transformers more robust than CNNs?
| 140 |
neurips
| 9 | 1 |
2023-06-16 16:08:17.098000
|
https://github.com/ytongbai/ViTs-vs-CNNs
| 157 |
Are transformers more robust than cnns?
|
https://scholar.google.com/scholar?cluster=2316302132679082774&hl=en&as_sdt=0,33
| 13 | 2,021 |
Automated Discovery of Adaptive Attacks on Adversarial Defenses
| 15 |
neurips
| 7 | 0 |
2023-06-16 16:08:17.299000
|
https://github.com/eth-sri/adaptive-auto-attack
| 23 |
Automated discovery of adaptive attacks on adversarial defenses
|
https://scholar.google.com/scholar?cluster=238969790812050690&hl=en&as_sdt=0,5
| 5 | 2,021 |
Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
| 14 |
neurips
| 0 | 0 |
2023-06-16 16:08:17.499000
|
https://github.com/meta-hg/metahg
| 10 |
Distilling meta knowledge on heterogeneous graph for illicit drug trafficker detection on social media
|
https://scholar.google.com/scholar?cluster=16874907594472944579&hl=en&as_sdt=0,44
| 1 | 2,021 |
Curriculum Disentangled Recommendation with Noisy Multi-feedback
| 20 |
neurips
| 3 | 0 |
2023-06-16 16:08:17.699000
|
https://github.com/forchchch/cdr
| 16 |
Curriculum disentangled recommendation with noisy multi-feedback
|
https://scholar.google.com/scholar?cluster=13030142921653638499&hl=en&as_sdt=0,33
| 1 | 2,021 |
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
| 13 |
neurips
| 98 | 7 |
2023-06-16 16:08:17.900000
|
https://github.com/facebookresearch/EGG
| 261 |
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
|
https://scholar.google.com/scholar?cluster=11916940036915302991&hl=en&as_sdt=0,50
| 16 | 2,021 |
MAU: A Motion-Aware Unit for Video Prediction and Beyond
| 27 |
neurips
| 8 | 1 |
2023-06-16 16:08:18.100000
|
https://github.com/ZhengChang467/MAU
| 23 |
Mau: A motion-aware unit for video prediction and beyond
|
https://scholar.google.com/scholar?cluster=9016601602145736560&hl=en&as_sdt=0,43
| 2 | 2,021 |
MagNet: A Neural Network for Directed Graphs
| 39 |
neurips
| 4 | 0 |
2023-06-16 16:08:18.301000
|
https://github.com/matthew-hirn/magnet
| 25 |
Magnet: A neural network for directed graphs
|
https://scholar.google.com/scholar?cluster=14949439358621371423&hl=en&as_sdt=0,33
| 4 | 2,021 |
Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning
| 10 |
neurips
| 7 | 1 |
2023-06-16 16:08:18.502000
|
https://github.com/hayeonlee/help
| 48 |
Hardware-adaptive efficient latency prediction for nas via meta-learning
|
https://scholar.google.com/scholar?cluster=1151236959431526951&hl=en&as_sdt=0,33
| 4 | 2,021 |
Topological Relational Learning on Graphs
| 15 |
neurips
| 0 | 1 |
2023-06-16 16:08:18.702000
|
https://github.com/tri-gnn/tri-gnn
| 10 |
Topological relational learning on graphs
|
https://scholar.google.com/scholar?cluster=11165869042107158625&hl=en&as_sdt=0,5
| 2 | 2,021 |
Least Square Calibration for Peer Reviews
| 243 |
neurips
| 0 | 0 |
2023-06-16 16:08:18.902000
|
https://github.com/lab-sigma/lsc
| 1 |
Generalization based on least squares adjustment
|
https://scholar.google.com/scholar?cluster=11630654823828571630&hl=en&as_sdt=0,22
| 0 | 2,021 |
Scaling Up Exact Neural Network Compression by ReLU Stability
| 11 |
neurips
| 0 | 0 |
2023-06-16 16:08:19.103000
|
https://github.com/yuxwind/ExactCompression
| 7 |
Scaling up exact neural network compression by ReLU stability
|
https://scholar.google.com/scholar?cluster=8701546882777093481&hl=en&as_sdt=0,15
| 1 | 2,021 |
Passive attention in artificial neural networks predicts human visual selectivity
| 14 |
neurips
| 2 | 0 |
2023-06-16 16:08:19.317000
|
https://github.com/czhao39/neurips-attention
| 5 |
Passive attention in artificial neural networks predicts human visual selectivity
|
https://scholar.google.com/scholar?cluster=2962365279533540728&hl=en&as_sdt=0,44
| 3 | 2,021 |
Instance-Dependent Partial Label Learning
| 33 |
neurips
| 3 | 0 |
2023-06-16 16:08:19.519000
|
https://github.com/palm-ml/valen
| 22 |
Instance-dependent partial label learning
|
https://scholar.google.com/scholar?cluster=15329270138955343757&hl=en&as_sdt=0,36
| 1 | 2,021 |
Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification
| 15 |
neurips
| 2 | 1 |
2023-06-16 16:08:19.720000
|
https://github.com/NeurIPS2021Paper4075/SemiMonDEQ
| 0 |
Semialgebraic representation of monotone deep equilibrium models and applications to certification
|
https://scholar.google.com/scholar?cluster=4954807623648783263&hl=en&as_sdt=0,16
| 1 | 2,021 |
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
| 405 |
neurips
| 168 | 64 |
2023-06-16 16:08:19.927000
|
https://github.com/Totoro97/NeuS
| 1,077 |
Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction
|
https://scholar.google.com/scholar?cluster=13663958172634895799&hl=en&as_sdt=0,33
| 22 | 2,021 |
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
| 9 |
neurips
| 3 | 0 |
2023-06-16 16:08:20.134000
|
https://github.com/suyoung-lee/ldm
| 15 |
Improving generalization in meta-rl with imaginary tasks from latent dynamics mixture
|
https://scholar.google.com/scholar?cluster=7863235735756161058&hl=en&as_sdt=0,5
| 1 | 2,021 |
Localization with Sampling-Argmax
| 5 |
neurips
| 6 | 5 |
2023-06-16 16:08:20.334000
|
https://github.com/Jeff-sjtu/sampling-argmax
| 80 |
Localization with sampling-argmax
|
https://scholar.google.com/scholar?cluster=16900151620493971528&hl=en&as_sdt=0,33
| 7 | 2,021 |
Improved Regularization and Robustness for Fine-tuning in Neural Networks
| 19 |
neurips
| 1 | 1 |
2023-06-16 16:08:20.535000
|
https://github.com/neu-statsml-research/regularized-self-labeling
| 24 |
Improved regularization and robustness for fine-tuning in neural networks
|
https://scholar.google.com/scholar?cluster=14262652923694182167&hl=en&as_sdt=0,49
| 2 | 2,021 |
BARTScore: Evaluating Generated Text as Text Generation
| 225 |
neurips
| 30 | 9 |
2023-06-16 16:08:20.735000
|
https://github.com/neulab/BARTScore
| 237 |
Bartscore: Evaluating generated text as text generation
|
https://scholar.google.com/scholar?cluster=8096338858323282474&hl=en&as_sdt=0,33
| 6 | 2,021 |
Robust Contrastive Learning Using Negative Samples with Diminished Semantics
| 42 |
neurips
| 8 | 0 |
2023-06-16 16:08:20.935000
|
https://github.com/SongweiGe/Contrastive-Learning-with-Non-Semantic-Negatives
| 40 |
Robust contrastive learning using negative samples with diminished semantics
|
https://scholar.google.com/scholar?cluster=7490092898284708794&hl=en&as_sdt=0,33
| 2 | 2,021 |
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
| 84 |
neurips
| 67 | 8 |
2023-06-16 16:08:21.152000
|
https://github.com/bengioe/gflownet
| 457 |
Flow network based generative models for non-iterative diverse candidate generation
|
https://scholar.google.com/scholar?cluster=8126213328674234815&hl=en&as_sdt=0,18
| 10 | 2,021 |
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation
| 62 |
neurips
| 5 | 6 |
2023-06-16 16:08:21.353000
|
https://github.com/jbeomlee93/rib
| 81 |
Reducing information bottleneck for weakly supervised semantic segmentation
|
https://scholar.google.com/scholar?cluster=1609158517855836438&hl=en&as_sdt=0,33
| 3 | 2,021 |
AC-GC: Lossy Activation Compression with Guaranteed Convergence
| 10 |
neurips
| 0 | 1 |
2023-06-16 16:08:21.553000
|
https://github.com/rdevans0/acgc
| 3 |
Ac-gc: Lossy activation compression with guaranteed convergence
|
https://scholar.google.com/scholar?cluster=1264227773571406457&hl=en&as_sdt=0,47
| 1 | 2,021 |
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
| 6 |
neurips
| 0 | 0 |
2023-06-16 16:08:21.753000
|
https://github.com/ksandeshk/spatial-vs-robustness
| 0 |
Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
|
https://scholar.google.com/scholar?cluster=15810468543209230356&hl=en&as_sdt=0,44
| 1 | 2,021 |
Universal Off-Policy Evaluation
| 33 |
neurips
| 1 | 1 |
2023-06-16 16:08:21.953000
|
https://github.com/yashchandak/UnO
| 3 |
Universal off-policy evaluation
|
https://scholar.google.com/scholar?cluster=15687557673143979580&hl=en&as_sdt=0,5
| 2 | 2,021 |
Efficiently Identifying Task Groupings for Multi-Task Learning
| 84 |
neurips
| 7,321 | 1,026 |
2023-06-16 16:08:22.154000
|
https://github.com/google-research/google-research
| 29,786 |
Efficiently identifying task groupings for multi-task learning
|
https://scholar.google.com/scholar?cluster=14971960796131955796&hl=en&as_sdt=0,14
| 727 | 2,021 |
Instance-Conditioned GAN
| 67 |
neurips
| 72 | 11 |
2023-06-16 16:08:22.354000
|
https://github.com/facebookresearch/ic_gan
| 520 |
Instance-conditioned gan
|
https://scholar.google.com/scholar?cluster=9688091502040853342&hl=en&as_sdt=0,33
| 20 | 2,021 |
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
| 6 |
neurips
| 0 | 0 |
2023-06-16 16:08:22.554000
|
https://github.com/compmem/deepsith
| 8 |
DeepSITH: Efficient learning via decomposition of what and when across time scales
|
https://scholar.google.com/scholar?cluster=9839987193236490170&hl=en&as_sdt=0,13
| 6 | 2,021 |
A Unified View of cGANs with and without Classifiers
| 6 |
neurips
| 2 | 0 |
2023-06-16 16:08:22.754000
|
https://github.com/sian-chen/pytorch-ecgan
| 24 |
A Unified View of cGANs with and without Classifiers
|
https://scholar.google.com/scholar?cluster=7864400027799016217&hl=en&as_sdt=0,33
| 3 | 2,021 |
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:08:22.954000
|
https://github.com/ssl-codelab/uota
| 6 |
Improving self-supervised learning with automated unsupervised outlier arbitration
|
https://scholar.google.com/scholar?cluster=16964194655596276571&hl=en&as_sdt=0,5
| 1 | 2,021 |
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:08:23.155000
|
https://github.com/michael-iuzzolino/CascadedNets
| 6 |
Improving anytime prediction with parallel cascaded networks and a temporal-difference loss
|
https://scholar.google.com/scholar?cluster=14093037979980851402&hl=en&as_sdt=0,10
| 2 | 2,021 |
Identifiable Generative models for Missing Not at Random Data Imputation
| 10 |
neurips
| 25 | 1 |
2023-06-16 16:08:23.356000
|
https://github.com/microsoft/project-azua
| 208 |
Identifiable generative models for missing not at random data imputation
|
https://scholar.google.com/scholar?cluster=3807116109136589039&hl=en&as_sdt=0,33
| 11 | 2,021 |
Local Hyper-Flow Diffusion
| 8 |
neurips
| 2 | 0 |
2023-06-16 16:08:23.558000
|
https://github.com/s-h-yang/HFD
| 2 |
Local hyper-flow diffusion
|
https://scholar.google.com/scholar?cluster=15981181330230884559&hl=en&as_sdt=0,21
| 1 | 2,021 |
Permuton-induced Chinese Restaurant Process
| 2 |
neurips
| 1 | 0 |
2023-06-16 16:08:23.766000
|
https://github.com/nttcslab/permuton-induced-crp
| 3 |
Permuton-induced Chinese restaurant process
|
https://scholar.google.com/scholar?cluster=15342887541779236192&hl=en&as_sdt=0,33
| 3 | 2,021 |
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error
| 1 |
neurips
| 2 | 0 |
2023-06-16 16:08:23.967000
|
https://github.com/justc2/worst-case-randomly-collected
| 3 |
Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error
|
https://scholar.google.com/scholar?cluster=5134119309073898368&hl=en&as_sdt=0,33
| 1 | 2,021 |
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game
| 38 |
neurips
| 1 | 0 |
2023-06-16 16:08:24.169000
|
https://github.com/scriddie/varsortability
| 12 |
Beware of the simulated dag! causal discovery benchmarks may be easy to game
|
https://scholar.google.com/scholar?cluster=15056583277700690862&hl=en&as_sdt=0,33
| 4 | 2,021 |
Robust Predictable Control
| 20 |
neurips
| 562 | 12 |
2023-06-16 16:08:24.370000
|
https://github.com/eleurent/highway-env
| 1,849 |
Robust predictable control
|
https://scholar.google.com/scholar?cluster=8057387371950805488&hl=en&as_sdt=0,33
| 23 | 2,021 |
Unsupervised Speech Recognition
| 173 |
neurips
| 5,878 | 1,030 |
2023-06-16 16:08:24.572000
|
https://github.com/pytorch/fairseq
| 26,479 |
Unsupervised speech recognition
|
https://scholar.google.com/scholar?cluster=7092177079954747232&hl=en&as_sdt=0,14
| 411 | 2,021 |
Online Learning and Control of Complex Dynamical Systems from Sensory Input
| 2 |
neurips
| 0 | 0 |
2023-06-16 16:08:24.773000
|
https://github.com/oumayb/online_dynamics_control
| 6 |
Online Learning and Control of Complex Dynamical Systems from Sensory Input
|
https://scholar.google.com/scholar?cluster=1383948933204770647&hl=en&as_sdt=0,5
| 1 | 2,021 |
Self-Supervised Bug Detection and Repair
| 56 |
neurips
| 19 | 6 |
2023-06-16 16:08:24.974000
|
https://github.com/microsoft/neurips21-self-supervised-bug-detection-and-repair
| 97 |
Self-supervised bug detection and repair
|
https://scholar.google.com/scholar?cluster=7144327257575633372&hl=en&as_sdt=0,33
| 12 | 2,021 |
Faster Neural Network Training with Approximate Tensor Operations
| 22 |
neurips
| 0 | 0 |
2023-06-16 16:08:25.179000
|
https://github.com/acsl-technion/approx
| 6 |
Faster neural network training with approximate tensor operations
|
https://scholar.google.com/scholar?cluster=14033293774816161034&hl=en&as_sdt=0,38
| 1 | 2,021 |
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
| 6 |
neurips
| 8 | 2 |
2023-06-16 16:08:25.380000
|
https://github.com/KellyYutongHe/satellite-pixel-synthesis-pytorch
| 24 |
Spatial-temporal super-resolution of satellite imagery via conditional pixel synthesis
|
https://scholar.google.com/scholar?cluster=15319459420045526884&hl=en&as_sdt=0,33
| 5 | 2,021 |
Garment4D: Garment Reconstruction from Point Cloud Sequences
| 9 |
neurips
| 17 | 4 |
2023-06-16 16:08:25.580000
|
https://github.com/hongfz16/garment4d
| 121 |
Garment4d: Garment reconstruction from point cloud sequences
|
https://scholar.google.com/scholar?cluster=2204817169651451344&hl=en&as_sdt=0,33
| 6 | 2,021 |
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data
| 28 |
neurips
| 6 | 3 |
2023-06-16 16:08:25.780000
|
https://github.com/gentlezhu/shift-robust-gnns
| 45 |
Shift-robust gnns: Overcoming the limitations of localized graph training data
|
https://scholar.google.com/scholar?cluster=13890659734687981736&hl=en&as_sdt=0,33
| 2 | 2,021 |
RIM: Reliable Influence-based Active Learning on Graphs
| 3 |
neurips
| 2 | 0 |
2023-06-16 16:08:25.980000
|
https://github.com/zwt233/rim
| 4 |
Rim: Reliable influence-based active learning on graphs
|
https://scholar.google.com/scholar?cluster=5200896252753882608&hl=en&as_sdt=0,14
| 2 | 2,021 |
Dynamical Wasserstein Barycenters for Time-series Modeling
| 4 |
neurips
| 1 | 0 |
2023-06-16 16:08:26.181000
|
https://github.com/kevin-c-cheng/dynamicalwassbarycenters_gaussian
| 9 |
Dynamical Wasserstein barycenters for time-series modeling
|
https://scholar.google.com/scholar?cluster=14561701553240392595&hl=en&as_sdt=0,11
| 1 | 2,021 |
RelaySum for Decentralized Deep Learning on Heterogeneous Data
| 33 |
neurips
| 2 | 0 |
2023-06-16 16:08:26.381000
|
https://github.com/epfml/relaysgd
| 6 |
Relaysum for decentralized deep learning on heterogeneous data
|
https://scholar.google.com/scholar?cluster=13522675478671696276&hl=en&as_sdt=0,33
| 6 | 2,021 |
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs
| 14 |
neurips
| 6 | 0 |
2023-06-16 16:08:26.583000
|
https://github.com/jw9730/hot
| 46 |
Transformers generalize deepsets and can be extended to graphs & hypergraphs
|
https://scholar.google.com/scholar?cluster=4459735355491111784&hl=en&as_sdt=0,33
| 1 | 2,021 |
Encoding Robustness to Image Style via Adversarial Feature Perturbations
| 5 |
neurips
| 2 | 0 |
2023-06-16 16:08:26.783000
|
https://github.com/azshue/AdvBN
| 9 |
Encoding robustness to image style via adversarial feature perturbations
|
https://scholar.google.com/scholar?cluster=6403103949061103720&hl=en&as_sdt=0,5
| 1 | 2,021 |
Natural continual learning: success is a journey, not (just) a destination
| 25 |
neurips
| 1 | 0 |
2023-06-16 16:08:26.984000
|
https://github.com/tachukao/ncl
| 7 |
Natural continual learning: success is a journey, not (just) a destination
|
https://scholar.google.com/scholar?cluster=14888388153938453691&hl=en&as_sdt=0,33
| 2 | 2,021 |
Unsupervised Part Discovery from Contrastive Reconstruction
| 33 |
neurips
| 6 | 3 |
2023-06-16 16:08:27.184000
|
https://github.com/subhc/unsup-parts
| 59 |
Unsupervised part discovery from contrastive reconstruction
|
https://scholar.google.com/scholar?cluster=5041027842313790381&hl=en&as_sdt=0,33
| 6 | 2,021 |
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning
| 26 |
neurips
| 2 | 2 |
2023-06-16 16:08:27.385000
|
https://github.com/guochengqian/assanet
| 30 |
Assanet: An anisotropic separable set abstraction for efficient point cloud representation learning
|
https://scholar.google.com/scholar?cluster=14172357416366632432&hl=en&as_sdt=0,33
| 6 | 2,021 |
Fair Sequential Selection Using Supervised Learning Models
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:08:27.585000
|
https://github.com/m0hammadmahdi/neurips2021_fair-sequential-selection-using-supervised-learning-models
| 0 |
Fair sequential selection using supervised learning models
|
https://scholar.google.com/scholar?cluster=1562219194987270101&hl=en&as_sdt=0,3
| 1 | 2,021 |
Towards Sample-efficient Overparameterized Meta-learning
| 16 |
neurips
| 0 | 0 |
2023-06-16 16:08:27.786000
|
https://github.com/sunyue93/rep-learning
| 0 |
Towards sample-efficient overparameterized meta-learning
|
https://scholar.google.com/scholar?cluster=7770324416491946595&hl=en&as_sdt=0,41
| 2 | 2,021 |
Independent mechanism analysis, a new concept?
| 38 |
neurips
| 5 | 1 |
2023-06-16 16:08:27.986000
|
https://github.com/lgresele/independent-mechanism-analysis
| 19 |
Independent mechanism analysis, a new concept?
|
https://scholar.google.com/scholar?cluster=3071675767973388187&hl=en&as_sdt=0,33
| 2 | 2,021 |
Robustness via Uncertainty-aware Cycle Consistency
| 9 |
neurips
| 4 | 0 |
2023-06-16 16:08:28.186000
|
https://github.com/explainableml/uncertaintyawarecycleconsistency
| 21 |
Robustness via uncertainty-aware cycle consistency
|
https://scholar.google.com/scholar?cluster=6383754569439233889&hl=en&as_sdt=0,36
| 5 | 2,021 |
CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method
| 3 |
neurips
| 0 | 0 |
2023-06-16 16:08:28.387000
|
https://github.com/dooseokjeong/cbp
| 1 |
CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method
|
https://scholar.google.com/scholar?cluster=7208237735280582675&hl=en&as_sdt=0,6
| 1 | 2,021 |
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
| 12 |
neurips
| 0 | 0 |
2023-06-16 16:08:28.587000
|
https://github.com/jiangyuan2li/implicit-sparse-regularization
| 1 |
Implicit sparse regularization: The impact of depth and early stopping
|
https://scholar.google.com/scholar?cluster=4712253773396003910&hl=en&as_sdt=0,33
| 3 | 2,021 |
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning
| 24 |
neurips
| 4 | 1 |
2023-06-16 16:08:28.787000
|
https://github.com/junsu-kim97/higl
| 27 |
Landmark-guided subgoal generation in hierarchical reinforcement learning
|
https://scholar.google.com/scholar?cluster=12842225468737823551&hl=en&as_sdt=0,33
| 2 | 2,021 |
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
| 15 |
neurips
| 0 | 0 |
2023-06-16 16:08:28.988000
|
https://github.com/conglu1997/nppac
| 7 |
On pathologies in KL-regularized reinforcement learning from expert demonstrations
|
https://scholar.google.com/scholar?cluster=13346980739265186497&hl=en&as_sdt=0,31
| 2 | 2,021 |
Conditional Generation Using Polynomial Expansions
| 9 |
neurips
| 0 | 0 |
2023-06-16 16:08:29.189000
|
https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation
| 6 |
Conditional generation using polynomial expansions
|
https://scholar.google.com/scholar?cluster=2570209794956894506&hl=en&as_sdt=0,33
| 2 | 2,021 |
Adaptive Online Packing-guided Search for POMDPs
| 6 |
neurips
| 3 | 0 |
2023-06-16 16:08:29.390000
|
https://github.com/lamda-pomdp/adaops.jl
| 9 |
Adaptive Online Packing-guided Search for POMDPs
|
https://scholar.google.com/scholar?cluster=1368812390956957164&hl=en&as_sdt=0,47
| 2 | 2,021 |
End-to-end Multi-modal Video Temporal Grounding
| 19 |
neurips
| 0 | 2 |
2023-06-16 16:08:29.596000
|
https://github.com/wenz116/drft
| 17 |
End-to-end multi-modal video temporal grounding
|
https://scholar.google.com/scholar?cluster=12383012058423217562&hl=en&as_sdt=0,33
| 5 | 2,021 |
How Powerful are Performance Predictors in Neural Architecture Search?
| 70 |
neurips
| 94 | 29 |
2023-06-16 16:08:29.797000
|
https://github.com/automl/NASLib
| 402 |
How powerful are performance predictors in neural architecture search?
|
https://scholar.google.com/scholar?cluster=14402357540412302091&hl=en&as_sdt=0,5
| 14 | 2,021 |
Stylized Dialogue Generation with Multi-Pass Dual Learning
| 7 |
neurips
| 0 | 3 |
2023-06-16 16:08:30.004000
|
https://github.com/codebaseli/mpdl
| 3 |
Stylized dialogue generation with multi-pass dual learning
|
https://scholar.google.com/scholar?cluster=11118854969470052027&hl=en&as_sdt=0,21
| 1 | 2,021 |
Entropy-based adaptive Hamiltonian Monte Carlo
| 4 |
neurips
| 0 | 0 |
2023-06-16 16:08:30.206000
|
https://github.com/marcelah/entropy_adaptive_hmc
| 1 |
Entropy-based adaptive hamiltonian monte carlo
|
https://scholar.google.com/scholar?cluster=3200582858390152415&hl=en&as_sdt=0,48
| 1 | 2,021 |
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
| 35 |
neurips
| 11 | 5 |
2023-06-16 16:08:30.407000
|
https://github.com/awarelab/continual_world
| 54 |
Continual world: A robotic benchmark for continual reinforcement learning
|
https://scholar.google.com/scholar?cluster=1195122932828127100&hl=en&as_sdt=0,5
| 3 | 2,021 |
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
| 181 |
neurips
| 15 | 3 |
2023-06-16 16:08:30.607000
|
https://github.com/Annbless/ViTAE
| 104 |
Vitae: Vision transformer advanced by exploring intrinsic inductive bias
|
https://scholar.google.com/scholar?cluster=14266701726231961165&hl=en&as_sdt=0,25
| 8 | 2,021 |
Open Rule Induction
| 4 |
neurips
| 3 | 0 |
2023-06-16 16:08:30.808000
|
https://github.com/chenxran/orion
| 18 |
Open rule induction
|
https://scholar.google.com/scholar?cluster=18275159905566382663&hl=en&as_sdt=0,50
| 1 | 2,021 |
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
| 17 |
neurips
| 6 | 2 |
2023-06-16 16:08:31.009000
|
https://github.com/sjleo/gcc
| 34 |
Revisiting discriminator in GAN compression: A generator-discriminator cooperative compression scheme
|
https://scholar.google.com/scholar?cluster=14200424528838121517&hl=en&as_sdt=0,33
| 3 | 2,021 |
Topographic VAEs learn Equivariant Capsules
| 16 |
neurips
| 14 | 2 |
2023-06-16 16:08:31.210000
|
https://github.com/akandykeller/topographicvae
| 72 |
Topographic vaes learn equivariant capsules
|
https://scholar.google.com/scholar?cluster=4234338937076957460&hl=en&as_sdt=0,32
| 3 | 2,021 |
MobILE: Model-Based Imitation Learning From Observation Alone
| 16 |
neurips
| 2 | 1 |
2023-06-16 16:08:31.412000
|
https://github.com/rahulkidambi/mobile-neurips2021
| 6 |
Mobile: Model-based imitation learning from observation alone
|
https://scholar.google.com/scholar?cluster=8914369701297657795&hl=en&as_sdt=0,5
| 2 | 2,021 |
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
| 7 |
neurips
| 2 | 0 |
2023-06-16 16:08:31.613000
|
https://github.com/ruiqigao/grid-cell-path
| 40 |
On path integration of grid cells: group representation and isotropic scaling
|
https://scholar.google.com/scholar?cluster=12036851998836312234&hl=en&as_sdt=0,44
| 2 | 2,021 |
Making a (Counterfactual) Difference One Rationale at a Time
| 3 |
neurips
| 1 | 1 |
2023-06-16 16:08:31.814000
|
https://github.com/mlplyler/cfs_for_rationales
| 5 |
Making a (Counterfactual) Difference One Rationale at a Time
|
https://scholar.google.com/scholar?cluster=641729738996559860&hl=en&as_sdt=0,5
| 1 | 2,021 |
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
| 29 |
neurips
| 4 | 5 |
2023-06-16 16:08:32.015000
|
https://github.com/fpthink/v2b
| 33 |
3D Siamese voxel-to-BEV tracker for sparse point clouds
|
https://scholar.google.com/scholar?cluster=3916550808113986620&hl=en&as_sdt=0,5
| 3 | 2,021 |
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
| 43 |
neurips
| 35 | 1 |
2023-06-16 16:08:32.215000
|
https://github.com/OATML/Non-Parametric-Transformers
| 370 |
Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
|
https://scholar.google.com/scholar?cluster=1349347196741730102&hl=en&as_sdt=0,5
| 9 | 2,021 |
On Contrastive Representations of Stochastic Processes
| 9 |
neurips
| 1 | 0 |
2023-06-16 16:08:32.416000
|
https://github.com/ae-foster/cresp
| 11 |
On contrastive representations of stochastic processes
|
https://scholar.google.com/scholar?cluster=14134769068028722426&hl=en&as_sdt=0,33
| 3 | 2,021 |
Scalars are universal: Equivariant machine learning, structured like classical physics
| 52 |
neurips
| 5 | 0 |
2023-06-16 16:08:32.617000
|
https://github.com/weichiyao/scalaremlp
| 14 |
Scalars are universal: Equivariant machine learning, structured like classical physics
|
https://scholar.google.com/scholar?cluster=15130731993267157989&hl=en&as_sdt=0,33
| 2 | 2,021 |
Unsupervised Object-Level Representation Learning from Scene Images
| 41 |
neurips
| 5 | 4 |
2023-06-16 16:08:32.818000
|
https://github.com/jiahao000/orl
| 56 |
Unsupervised object-level representation learning from scene images
|
https://scholar.google.com/scholar?cluster=11947642466448713378&hl=en&as_sdt=0,43
| 1 | 2,021 |
Stronger NAS with Weaker Predictors
| 16 |
neurips
| 6 | 1 |
2023-06-16 16:08:33.018000
|
https://github.com/VITA-Group/WeakNAS
| 21 |
Stronger nas with weaker predictors
|
https://scholar.google.com/scholar?cluster=7907486067931275084&hl=en&as_sdt=0,33
| 10 | 2,021 |
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
| 17 |
neurips
| 5 | 0 |
2023-06-16 16:08:33.218000
|
https://github.com/shengliu66/ConvNorm
| 26 |
Convolutional normalization: Improving deep convolutional network robustness and training
|
https://scholar.google.com/scholar?cluster=2251331511068092550&hl=en&as_sdt=0,33
| 2 | 2,021 |
On the Expected Complexity of Maxout Networks
| 5 |
neurips
| 0 | 0 |
2023-06-16 16:08:33.418000
|
https://github.com/hanna-tseran/maxout_complexity
| 0 |
On the expected complexity of maxout networks
|
https://scholar.google.com/scholar?cluster=17674952708371009223&hl=en&as_sdt=0,5
| 1 | 2,021 |
Can multi-label classification networks know what they don’t know?
| 53 |
neurips
| 4 | 4 |
2023-06-16 16:08:33.620000
|
https://github.com/deeplearning-wisc/multi-label-ood
| 31 |
Can multi-label classification networks know what they don't know?
|
https://scholar.google.com/scholar?cluster=7813141666624240186&hl=en&as_sdt=0,19
| 1 | 2,021 |
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