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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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