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PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark | 34 | eccv | 60 | 8 | 2023-06-17 01:00:14.470000 | https://github.com/OpenDriveLab/PersFormer_3DLane | 302 | Persformer: 3d lane detection via perspective transformer and the openlane benchmark | https://scholar.google.com/scholar?cluster=15083699818412600037&hl=en&as_sdt=0,34 | 13 | 2,022 |
Context-Aware Streaming Perception in Dynamic Environments | 1 | eccv | 0 | 1 | 2023-06-17 01:00:14.682000 | https://github.com/eyalsel/contextual-streaming-perception | 1 | Context-Aware Streaming Perception in Dynamic Environments | https://scholar.google.com/scholar?cluster=10274140893986242445&hl=en&as_sdt=0,32 | 1 | 2,022 |
Multimodal Transformer for Automatic 3D Annotation and Object Detection | 1 | eccv | 3 | 1 | 2023-06-17 01:00:14.894000 | https://github.com/cliu2/mtrans | 24 | Multimodal Transformer for Automatic 3D Annotation and Object Detection | https://scholar.google.com/scholar?cluster=969973501768812021&hl=en&as_sdt=0,31 | 4 | 2,022 |
Dynamic 3D Scene Analysis by Point Cloud Accumulation | 10 | eccv | 9 | 2 | 2023-06-17 01:00:15.120000 | https://github.com/prs-eth/PCAccumulation | 101 | Dynamic 3D Scene Analysis by Point Cloud Accumulation | https://scholar.google.com/scholar?cluster=5413156346707772700&hl=en&as_sdt=0,6 | 6 | 2,022 |
Semi-Supervised 3D Object Detection with Proficient Teachers | 19 | eccv | 0 | 7 | 2023-06-17 01:00:15.760000 | https://github.com/yinjunbo/proficientteachers | 16 | Semi-supervised 3D object detection with proficient teachers | https://scholar.google.com/scholar?cluster=14317699278856200063&hl=en&as_sdt=0,34 | 10 | 2,022 |
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection | 32 | eccv | 2 | 2 | 2023-06-17 01:00:16.048000 | https://github.com/yinjunbo/proposalcontrast | 36 | ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection | https://scholar.google.com/scholar?cluster=18192635711712436925&hl=en&as_sdt=0,34 | 7 | 2,022 |
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map | 5 | eccv | 1 | 2 | 2023-06-17 01:00:16.308000 | https://github.com/chenfengxu714/pretram | 14 | Pretram: Self-supervised pre-training via connecting trajectory and map | https://scholar.google.com/scholar?cluster=15409489933369373248&hl=en&as_sdt=0,34 | 4 | 2,022 |
Visual Cross-View Metric Localization with Dense Uncertainty Estimates | 7 | eccv | 0 | 0 | 2023-06-17 01:00:16.548000 | https://github.com/tudelft-iv/crossviewmetriclocalization | 20 | Visual cross-view metric localization with dense uncertainty estimates | https://scholar.google.com/scholar?cluster=12330293739091912034&hl=en&as_sdt=0,31 | 2 | 2,022 |
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer | 79 | eccv | 25 | 2 | 2023-06-17 01:00:16.767000 | https://github.com/DerrickXuNu/v2x-vit | 197 | V2X-ViT: Vehicle-to-everything cooperative perception with vision transformer | https://scholar.google.com/scholar?cluster=15088728781552938978&hl=en&as_sdt=0,36 | 4 | 2,022 |
DevNet: Self-Supervised Monocular Depth Learning via Density Volume Construction | 5 | eccv | 0 | 1 | 2023-06-17 01:00:16.987000 | https://github.com/gitkaichenzhou/devnet | 9 | Devnet: Self-supervised monocular depth learning via density volume construction | https://scholar.google.com/scholar?cluster=13357376187012706198&hl=en&as_sdt=0,5 | 4 | 2,022 |
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection | 19 | eccv | 8 | 5 | 2023-06-17 01:00:17.200000 | https://github.com/weiyithu/lidar-distillation | 85 | LiDAR distillation: bridging the beam-induced domain Gap for 3D object detection | https://scholar.google.com/scholar?cluster=587459598663659263&hl=en&as_sdt=0,5 | 7 | 2,022 |
Pixel-Wise Energy-Biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes | 21 | eccv | 18 | 0 | 2023-06-17 01:00:17.414000 | https://github.com/tianyu0207/pebal | 125 | Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes | https://scholar.google.com/scholar?cluster=828162099730136684&hl=en&as_sdt=0,5 | 5 | 2,022 |
Housekeep: Tidying Virtual Households Using Commonsense Reasoning | 19 | eccv | 5 | 0 | 2023-06-17 01:00:17.628000 | https://github.com/yashkant/housekeep | 27 | Housekeep: Tidying virtual households using commonsense reasoning | https://scholar.google.com/scholar?cluster=17323819814788144115&hl=en&as_sdt=0,41 | 5 | 2,022 |
Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects | 3 | eccv | 4 | 2 | 2023-06-17 01:00:17.841000 | https://github.com/pku-epic/dreds | 69 | Domain randomization-enhanced depth simulation and restoration for perceiving and grasping specular and transparent objects | https://scholar.google.com/scholar?cluster=4212070645420757381&hl=en&as_sdt=0,33 | 2 | 2,022 |
OPD: Single-View 3D Openable Part Detection | 8 | eccv | 3 | 0 | 2023-06-17 01:00:18.054000 | https://github.com/3dlg-hcvc/OPD | 19 | OPD: Single-view 3D openable part detection | https://scholar.google.com/scholar?cluster=1442718459307012446&hl=en&as_sdt=0,25 | 2 | 2,022 |
AirDet: Few-Shot Detection without Fine-Tuning for Autonomous Exploration | 8 | eccv | 7 | 9 | 2023-06-17 01:00:18.270000 | https://github.com/jaraxxus-me/airdet | 56 | Airdet: Few-shot detection without fine-tuning for autonomous exploration | https://scholar.google.com/scholar?cluster=13310465653301998245&hl=en&as_sdt=0,33 | 4 | 2,022 |
TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance | 2 | eccv | 3 | 0 | 2023-06-17 01:00:18.483000 | https://github.com/yanjh97/transgrasp | 26 | TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance | https://scholar.google.com/scholar?cluster=3147703943715388736&hl=en&as_sdt=0,14 | 1 | 2,022 |
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning | 6 | eccv | 8 | 0 | 2023-06-17 01:00:18.697000 | https://github.com/elicassion/StARformer | 54 | StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning | https://scholar.google.com/scholar?cluster=14349342012592165154&hl=en&as_sdt=0,10 | 4 | 2,022 |
Zero-Shot Category-Level Object Pose Estimation | 10 | eccv | 1 | 1 | 2023-06-17 01:00:18.911000 | https://github.com/applied-ai-lab/zero-shot-pose | 43 | Zero-shot category-level object pose estimation | https://scholar.google.com/scholar?cluster=9047203948478183820&hl=en&as_sdt=0,44 | 6 | 2,022 |
Style-Agnostic Reinforcement Learning | 0 | eccv | 0 | 0 | 2023-06-17 01:00:19.127000 | https://github.com/postech-cvlab/style-agnostic-rl | 14 | Style-Agnostic Reinforcement Learning | https://scholar.google.com/scholar?cluster=4525773551309192298&hl=en&as_sdt=0,47 | 5 | 2,022 |
Learning from Unlabeled 3D Environments for Vision-and-Language Navigation | 8 | eccv | 1 | 2 | 2023-06-17 01:00:19.339000 | https://github.com/cshizhe/HM3DAutoVLN | 16 | Learning from unlabeled 3d environments for vision-and-language navigation | https://scholar.google.com/scholar?cluster=16234245640110224024&hl=en&as_sdt=0,10 | 1 | 2,022 |
Video Dialog As Conversation about Objects Living in Space-Time | 2 | eccv | 1 | 1 | 2023-06-17 01:00:19.551000 | https://github.com/hoanganhpham1006/cost | 29 | Video Dialog as Conversation About Objects Living in Space-Time | https://scholar.google.com/scholar?cluster=7360692861127476051&hl=en&as_sdt=0,31 | 1 | 2,022 |
INSPECTRE: Privately Estimating the Unseen | 24 | icml | 2 | 0 | 2023-06-17 02:59:19.495000 | https://github.com/HuanyuZhang/INSPECTRE | 4 | Inspectre: Privately estimating the unseen | https://scholar.google.com/scholar?cluster=17397677821917989513&hl=en&as_sdt=0,34 | 5 | 2,018 |
Learning Representations and Generative Models for 3D Point Clouds | 1,057 | icml | 103 | 14 | 2023-06-17 02:59:19.714000 | https://github.com/optas/latent_3d_points | 468 | Learning representations and generative models for 3d point clouds | https://scholar.google.com/scholar?cluster=9902857073066842718&hl=en&as_sdt=0,30 | 15 | 2,018 |
Accelerated Spectral Ranking | 51 | icml | 0 | 0 | 2023-06-17 02:59:19.927000 | https://github.com/agarpit/asr | 0 | Accelerated spectral ranking | https://scholar.google.com/scholar?cluster=17059082101801262373&hl=en&as_sdt=0,5 | 1 | 2,018 |
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches | 41 | icml | 6 | 2 | 2023-06-17 02:59:20.141000 | https://github.com/rdspring1/MISSION | 12 | Mission: Ultra large-scale feature selection using count-sketches | https://scholar.google.com/scholar?cluster=7532201827567458901&hl=en&as_sdt=0,29 | 6 | 2,018 |
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory | 155 | icml | 5 | 1 | 2023-06-17 02:59:20.355000 | https://github.com/ron-amit/meta-learning-adjusting-priors | 22 | Meta-learning by adjusting priors based on extended PAC-Bayes theory | https://scholar.google.com/scholar?cluster=7282416635315381727&hl=en&as_sdt=0,21 | 2 | 2,018 |
MAGAN: Aligning Biological Manifolds | 71 | icml | 4 | 5 | 2023-06-17 02:59:20.569000 | https://github.com/KrishnaswamyLab/MAGAN | 17 | MAGAN: Aligning biological manifolds | https://scholar.google.com/scholar?cluster=2850609560851515473&hl=en&as_sdt=0,5 | 7 | 2,018 |
Efficient Gradient-Free Variational Inference using Policy Search | 30 | icml | 10 | 0 | 2023-06-17 02:59:20.783000 | https://github.com/OlegArenz/VIPS | 13 | Efficient gradient-free variational inference using policy search | https://scholar.google.com/scholar?cluster=15860909759042559191&hl=en&as_sdt=0,36 | 1 | 2,018 |
Lipschitz Continuity in Model-based Reinforcement Learning | 128 | icml | 1 | 0 | 2023-06-17 02:59:20.997000 | https://github.com/kavosh8/Lip | 11 | Lipschitz continuity in model-based reinforcement learning | https://scholar.google.com/scholar?cluster=7519868301941005316&hl=en&as_sdt=0,21 | 2 | 2,018 |
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | 2,845 | icml | 165 | 0 | 2023-06-17 02:59:21.212000 | https://github.com/anishathalye/obfuscated-gradients | 846 | Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples | https://scholar.google.com/scholar?cluster=16371153415378772336&hl=en&as_sdt=0,5 | 51 | 2,018 |
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing | 81 | icml | 10 | 1 | 2023-06-17 02:59:21.425000 | https://github.com/diningphil/CGMM | 35 | Contextual graph markov model: A deep and generative approach to graph processing | https://scholar.google.com/scholar?cluster=11762309887012905485&hl=en&as_sdt=0,47 | 5 | 2,018 |
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising | 272 | icml | 17 | 0 | 2023-06-17 02:59:21.640000 | https://github.com/BorjaBalle/analytic-gaussian-mechanism | 38 | Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising | https://scholar.google.com/scholar?cluster=6616371088385060239&hl=en&as_sdt=0,15 | 5 | 2,018 |
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients | 128 | icml | 2 | 1 | 2023-06-17 02:59:21.855000 | https://github.com/lballes/msvag | 44 | Dissecting adam: The sign, magnitude and variance of stochastic gradients | https://scholar.google.com/scholar?cluster=7051163857828136426&hl=en&as_sdt=0,5 | 5 | 2,018 |
Differentially Private Database Release via Kernel Mean Embeddings | 34 | icml | 5 | 0 | 2023-06-17 02:59:22.073000 | https://github.com/matejbalog/RKHS-private-database | 9 | Differentially private database release via kernel mean embeddings | https://scholar.google.com/scholar?cluster=3884748492191157354&hl=en&as_sdt=0,47 | 3 | 2,018 |
Classification from Pairwise Similarity and Unlabeled Data | 70 | icml | 7 | 0 | 2023-06-17 02:59:22.287000 | https://github.com/levelfour/SU_Classification | 27 | Classification from pairwise similarity and unlabeled data | https://scholar.google.com/scholar?cluster=8079423244693933514&hl=en&as_sdt=0,23 | 4 | 2,018 |
Bayesian Optimization of Combinatorial Structures | 115 | icml | 27 | 1 | 2023-06-17 02:59:22.500000 | https://github.com/baptistar/BOCS | 89 | Bayesian optimization of combinatorial structures | https://scholar.google.com/scholar?cluster=1602326552169762893&hl=en&as_sdt=0,5 | 5 | 2,018 |
signSGD: Compressed Optimisation for Non-Convex Problems | 763 | icml | 17 | 2 | 2023-06-17 02:59:22.714000 | https://github.com/jxbz/signSGD | 68 | signSGD: Compressed optimisation for non-convex problems | https://scholar.google.com/scholar?cluster=2554335502701113649&hl=en&as_sdt=0,14 | 4 | 2,018 |
Autoregressive Convolutional Neural Networks for Asynchronous Time Series | 161 | icml | 66 | 6 | 2023-06-17 02:59:22.927000 | https://github.com/mbinkowski/nntimeseries | 207 | Autoregressive convolutional neural networks for asynchronous time series | https://scholar.google.com/scholar?cluster=16946741031490973459&hl=en&as_sdt=0,11 | 15 | 2,018 |
Path-Level Network Transformation for Efficient Architecture Search | 218 | icml | 21 | 5 | 2023-06-17 02:59:23.149000 | https://github.com/han-cai/PathLevel-EAS | 113 | Path-level network transformation for efficient architecture search | https://scholar.google.com/scholar?cluster=17606554867892755331&hl=en&as_sdt=0,9 | 5 | 2,018 |
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent | 113 | icml | 30 | 1 | 2023-06-17 02:59:23.364000 | https://github.com/trevorcampbell/bayesian-coresets | 124 | Bayesian coreset construction via greedy iterative geodesic ascent | https://scholar.google.com/scholar?cluster=662866254282688281&hl=en&as_sdt=0,33 | 8 | 2,018 |
Adversarial Time-to-Event Modeling | 101 | icml | 11 | 0 | 2023-06-17 02:59:23.577000 | https://github.com/paidamoyo/adversarial_time_to_event | 35 | Adversarial time-to-event modeling | https://scholar.google.com/scholar?cluster=2862325105848484148&hl=en&as_sdt=0,28 | 4 | 2,018 |
Stein Points | 99 | icml | 1 | 1 | 2023-06-17 02:59:23.791000 | https://github.com/wilson-ye-chen/stein_points | 2 | Stein points | https://scholar.google.com/scholar?cluster=9019835252196634623&hl=en&as_sdt=0,5 | 0 | 2,018 |
PixelSNAIL: An Improved Autoregressive Generative Model | 203 | icml | 23 | 4 | 2023-06-17 02:59:24.005000 | https://github.com/neocxi/pixelsnail-public | 122 | Pixelsnail: An improved autoregressive generative model | https://scholar.google.com/scholar?cluster=3510281947390800354&hl=en&as_sdt=0,31 | 5 | 2,018 |
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation | 455 | icml | 36 | 3 | 2023-06-17 02:59:24.219000 | https://github.com/Jianbo-Lab/L2X | 118 | Learning to explain: An information-theoretic perspective on model interpretation | https://scholar.google.com/scholar?cluster=8716068966978529202&hl=en&as_sdt=0,36 | 12 | 2,018 |
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients | 211 | icml | 11 | 2 | 2023-06-17 02:59:24.433000 | https://github.com/hwang595/Draco | 21 | Draco: Byzantine-resilient distributed training via redundant gradients | https://scholar.google.com/scholar?cluster=7533143184939579191&hl=en&as_sdt=0,19 | 8 | 2,018 |
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms | 146 | icml | 6 | 0 | 2023-06-17 02:59:24.646000 | https://github.com/flowersteam/geppg | 36 | Gep-pg: Decoupling exploration and exploitation in deep reinforcement learning algorithms | https://scholar.google.com/scholar?cluster=13798285446369315971&hl=en&as_sdt=0,47 | 11 | 2,018 |
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation | 63 | icml | 3 | 2 | 2023-06-17 02:59:24.861000 | https://github.com/danecor/VaST | 13 | Efficient model-based deep reinforcement learning with variational state tabulation | https://scholar.google.com/scholar?cluster=1130683550787496400&hl=en&as_sdt=0,33 | 3 | 2,018 |
Asynchronous Byzantine Machine Learning (the case of SGD) | 99 | icml | 0 | 1 | 2023-06-17 02:59:25.075000 | https://github.com/LPD-EPFL/kardam | 0 | Asynchronous Byzantine machine learning (the case of SGD) | https://scholar.google.com/scholar?cluster=7761726425458216568&hl=en&as_sdt=0,33 | 5 | 2,018 |
Stochastic Video Generation with a Learned Prior | 453 | icml | 54 | 15 | 2023-06-17 02:59:25.289000 | https://github.com/edenton/svg | 173 | Stochastic video generation with a learned prior | https://scholar.google.com/scholar?cluster=9440265505324516729&hl=en&as_sdt=0,5 | 6 | 2,018 |
Probabilistic Recurrent State-Space Models | 109 | icml | 19 | 3 | 2023-06-17 02:59:25.503000 | https://github.com/andreasdoerr/PR-SSM | 48 | Probabilistic recurrent state-space models | https://scholar.google.com/scholar?cluster=13376246686422250291&hl=en&as_sdt=0,46 | 14 | 2,018 |
Essentially No Barriers in Neural Network Energy Landscape | 299 | icml | 11 | 1 | 2023-06-17 02:59:25.717000 | https://github.com/fdraxler/PyTorch-AutoNEB | 45 | Essentially no barriers in neural network energy landscape | https://scholar.google.com/scholar?cluster=15426527759025848933&hl=en&as_sdt=0,5 | 4 | 2,018 |
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures | 1,282 | icml | 162 | 14 | 2023-06-17 02:59:25.931000 | https://github.com/deepmind/scalable_agent | 948 | Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures | https://scholar.google.com/scholar?cluster=14673826846490570917&hl=en&as_sdt=0,10 | 36 | 2,018 |
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) | 28 | icml | 2 | 0 | 2023-06-17 02:59:26.145000 | https://github.com/treforevans/gp_grief | 22 | Scalable Gaussian processes with grid-structured eigenfunctions (GP-GRIEF) | https://scholar.google.com/scholar?cluster=16145188730835971053&hl=en&as_sdt=0,10 | 6 | 2,018 |
BOHB: Robust and Efficient Hyperparameter Optimization at Scale | 913 | icml | 113 | 65 | 2023-06-17 02:59:26.358000 | https://github.com/automl/HpBandSter | 576 | BOHB: Robust and efficient hyperparameter optimization at scale | https://scholar.google.com/scholar?cluster=7414210775058292852&hl=en&as_sdt=0,5 | 27 | 2,018 |
Nonparametric variable importance using an augmented neural network with multi-task learning | 16 | icml | 1 | 2 | 2023-06-17 02:59:26.573000 | https://github.com/jjfeng/nnet_var_import | 2 | Nonparametric variable importance using an augmented neural network with multi-task learning | https://scholar.google.com/scholar?cluster=626109963873101656&hl=en&as_sdt=0,33 | 4 | 2,018 |
DiCE: The Infinitely Differentiable Monte Carlo Estimator | 78 | icml | 36 | 8 | 2023-06-17 02:59:26.787000 | https://github.com/alshedivat/lola | 133 | Dice: The infinitely differentiable monte carlo estimator | https://scholar.google.com/scholar?cluster=9790220931943601676&hl=en&as_sdt=0,5 | 12 | 2,018 |
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning | 93 | icml | 5 | 0 | 2023-06-17 02:59:27.001000 | https://github.com/RonanFR/UCRL | 25 | Efficient bias-span-constrained exploration-exploitation in reinforcement learning | https://scholar.google.com/scholar?cluster=10255005828513027230&hl=en&as_sdt=0,47 | 5 | 2,018 |
Addressing Function Approximation Error in Actor-Critic Methods | 3,516 | icml | 393 | 4 | 2023-06-17 02:59:27.217000 | https://github.com/sfujim/TD3 | 1,371 | Addressing function approximation error in actor-critic methods | https://scholar.google.com/scholar?cluster=2930747733592680111&hl=en&as_sdt=0,5 | 19 | 2,018 |
Clipped Action Policy Gradient | 37 | icml | 1 | 0 | 2023-06-17 02:59:27.430000 | https://github.com/pfnet-research/capg | 26 | Clipped action policy gradient | https://scholar.google.com/scholar?cluster=14045811367797105459&hl=en&as_sdt=0,34 | 15 | 2,018 |
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings | 213 | icml | 11 | 1 | 2023-06-17 02:59:27.644000 | https://github.com/dalab/hyperbolic_cones | 122 | Hyperbolic entailment cones for learning hierarchical embeddings | https://scholar.google.com/scholar?cluster=18219062814600908733&hl=en&as_sdt=0,5 | 13 | 2,018 |
Visualizing and Understanding Atari Agents | 306 | icml | 34 | 5 | 2023-06-17 02:59:27.858000 | https://github.com/greydanus/visualize_atari | 114 | Visualizing and understanding atari agents | https://scholar.google.com/scholar?cluster=2974426333741298395&hl=en&as_sdt=0,5 | 2 | 2,018 |
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor | 5,442 | icml | 216 | 10 | 2023-06-17 02:59:28.073000 | https://github.com/haarnoja/sac | 802 | Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor | https://scholar.google.com/scholar?cluster=13282174879342015249&hl=en&as_sdt=0,5 | 30 | 2,018 |
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning | 11 | icml | 4 | 0 | 2023-06-17 02:59:28.287000 | https://github.com/jihunhamm/k-beam-minimax | 11 | K-beam minimax: Efficient optimization for deep adversarial learning | https://scholar.google.com/scholar?cluster=17252205883133016426&hl=en&as_sdt=0,1 | 2 | 2,018 |
Deep Models of Interactions Across Sets | 137 | icml | 4 | 3 | 2023-06-17 02:59:28.501000 | https://github.com/mravanba/deep_exchangeable_tensors | 9 | Deep models of interactions across sets | https://scholar.google.com/scholar?cluster=9552429858443331211&hl=en&as_sdt=0,33 | 5 | 2,018 |
Learning unknown ODE models with Gaussian processes | 64 | icml | 3 | 0 | 2023-06-17 02:59:28.714000 | https://github.com/cagatayyildiz/npode | 19 | Learning unknown ODE models with Gaussian processes | https://scholar.google.com/scholar?cluster=5804235817829238713&hl=en&as_sdt=0,5 | 4 | 2,018 |
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform | 111 | icml | 1 | 1 | 2023-06-17 02:59:28.928000 | https://github.com/SpartinStuff/scoRNN | 10 | Orthogonal recurrent neural networks with scaled Cayley transform | https://scholar.google.com/scholar?cluster=10576322947857760953&hl=en&as_sdt=0,23 | 3 | 2,018 |
CyCADA: Cycle-Consistent Adversarial Domain Adaptation | 2,682 | icml | 126 | 15 | 2023-06-17 02:59:29.142000 | https://github.com/jhoffman/cycada_release | 537 | Cycada: Cycle-consistent adversarial domain adaptation | https://scholar.google.com/scholar?cluster=13169730024102659375&hl=en&as_sdt=0,26 | 18 | 2,018 |
Neural Autoregressive Flows | 417 | icml | 28 | 2 | 2023-06-17 02:59:29.355000 | https://github.com/CW-Huang/NAF | 116 | Neural autoregressive flows | https://scholar.google.com/scholar?cluster=12117495056265504475&hl=en&as_sdt=0,33 | 12 | 2,018 |
Topological mixture estimation | 5 | icml | 0 | 0 | 2023-06-17 02:59:29.570000 | https://github.com/SteveHuntsmanBAESystems/TopologicalMixtureEstimation | 0 | Topological mixture estimation | https://scholar.google.com/scholar?cluster=5144775238736361207&hl=en&as_sdt=0,5 | 1 | 2,018 |
Decoupled Parallel Backpropagation with Convergence Guarantee | 69 | icml | 4 | 2 | 2023-06-17 02:59:29.783000 | https://github.com/slowbull/DDG | 28 | Decoupled parallel backpropagation with convergence guarantee | https://scholar.google.com/scholar?cluster=9542708515407168556&hl=en&as_sdt=0,43 | 4 | 2,018 |
Deep Variational Reinforcement Learning for POMDPs | 244 | icml | 26 | 5 | 2023-06-17 02:59:29.997000 | https://github.com/maximilianigl/DVRL | 123 | Deep variational reinforcement learning for POMDPs | https://scholar.google.com/scholar?cluster=12007406566032573768&hl=en&as_sdt=0,5 | 7 | 2,018 |
Attention-based Deep Multiple Instance Learning | 1,102 | icml | 174 | 12 | 2023-06-17 02:59:30.211000 | https://github.com/AMLab-Amsterdam/AttentionDeepMIL | 651 | Attention-based deep multiple instance learning | https://scholar.google.com/scholar?cluster=10689360653942822671&hl=en&as_sdt=0,33 | 16 | 2,018 |
Black-box Adversarial Attacks with Limited Queries and Information | 966 | icml | 44 | 12 | 2023-06-17 02:59:30.425000 | https://github.com/labsix/limited-blackbox-attacks | 166 | Black-box adversarial attacks with limited queries and information | https://scholar.google.com/scholar?cluster=15556405409493863238&hl=en&as_sdt=0,5 | 9 | 2,018 |
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model | 25 | icml | 1 | 0 | 2023-06-17 02:59:30.640000 | https://github.com/HideakiImamura/MinimaxErrorRate | 5 | Analysis of minimax error rate for crowdsourcing and its application to worker clustering model | https://scholar.google.com/scholar?cluster=7703940495110454435&hl=en&as_sdt=0,5 | 1 | 2,018 |
Anonymous Walk Embeddings | 183 | icml | 22 | 3 | 2023-06-17 02:59:30.854000 | https://github.com/nd7141/AWE | 78 | Anonymous walk embeddings | https://scholar.google.com/scholar?cluster=14558299451586877033&hl=en&as_sdt=0,33 | 6 | 2,018 |
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach | 15 | icml | 0 | 0 | 2023-06-17 02:59:31.069000 | https://github.com/arJaffe/BinaryLatentVariables | 1 | Learning binary latent variable models: A tensor eigenpair approach | https://scholar.google.com/scholar?cluster=2293549350827967377&hl=en&as_sdt=0,14 | 1 | 2,018 |
Efficient end-to-end learning for quantizable representations | 14 | icml | 14 | 0 | 2023-06-17 02:59:31.283000 | https://github.com/maestrojeong/Deep-Hash-Table-ICML18 | 66 | Efficient end-to-end learning for quantizable representations | https://scholar.google.com/scholar?cluster=14118214895723382983&hl=en&as_sdt=0,5 | 7 | 2,018 |
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift | 27 | icml | 20 | 1 | 2023-06-17 02:59:31.497000 | https://github.com/google/quickshift | 62 | Quickshift++: Provably good initializations for sample-based mean shift | https://scholar.google.com/scholar?cluster=9290981772171127937&hl=en&as_sdt=0,5 | 8 | 2,018 |
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels | 1,228 | icml | 67 | 5 | 2023-06-17 02:59:31.710000 | https://github.com/google/mentornet | 308 | Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels | https://scholar.google.com/scholar?cluster=18276912967596258717&hl=en&as_sdt=0,33 | 13 | 2,018 |
Junction Tree Variational Autoencoder for Molecular Graph Generation | 1,068 | icml | 182 | 30 | 2023-06-17 02:59:31.925000 | https://github.com/wengong-jin/icml18-jtnn | 439 | Junction tree variational autoencoder for molecular graph generation | https://scholar.google.com/scholar?cluster=14713480171095443338&hl=en&as_sdt=0,47 | 20 | 2,018 |
Not All Samples Are Created Equal: Deep Learning with Importance Sampling | 369 | icml | 59 | 9 | 2023-06-17 02:59:32.138000 | https://github.com/idiap/importance-sampling | 300 | Not all samples are created equal: Deep learning with importance sampling | https://scholar.google.com/scholar?cluster=6287347937947055060&hl=en&as_sdt=0,5 | 15 | 2,018 |
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness | 637 | icml | 10 | 1 | 2023-06-17 02:59:32.351000 | https://github.com/algowatchpenn/GerryFair | 31 | Preventing fairness gerrymandering: Auditing and learning for subgroup fairness | https://scholar.google.com/scholar?cluster=15519719606954445162&hl=en&as_sdt=0,23 | 7 | 2,018 |
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam | 231 | icml | 26 | 3 | 2023-06-17 02:59:32.565000 | https://github.com/emtiyaz/vadam | 107 | Fast and scalable bayesian deep learning by weight-perturbation in adam | https://scholar.google.com/scholar?cluster=11374390410783252644&hl=en&as_sdt=0,5 | 10 | 2,018 |
Geometry Score: A Method For Comparing Generative Adversarial Networks | 98 | icml | 21 | 1 | 2023-06-17 02:59:32.778000 | https://github.com/KhrulkovV/geometry-score | 113 | Geometry score: A method for comparing generative adversarial networks | https://scholar.google.com/scholar?cluster=3414107301309460899&hl=en&as_sdt=0,5 | 4 | 2,018 |
Blind Justice: Fairness with Encrypted Sensitive Attributes | 120 | icml | 3 | 1 | 2023-06-17 02:59:32.993000 | https://github.com/nikikilbertus/blind-justice | 14 | Blind justice: Fairness with encrypted sensitive attributes | https://scholar.google.com/scholar?cluster=7640712824806028167&hl=en&as_sdt=0,5 | 5 | 2,018 |
Semi-Amortized Variational Autoencoders | 246 | icml | 16 | 3 | 2023-06-17 02:59:33.207000 | https://github.com/harvardnlp/sa-vae | 153 | Semi-amortized variational autoencoders | https://scholar.google.com/scholar?cluster=15696369664604442539&hl=en&as_sdt=0,46 | 10 | 2,018 |
Neural Relational Inference for Interacting Systems | 710 | icml | 156 | 22 | 2023-06-17 02:59:33.430000 | https://github.com/ethanfetaya/nri | 681 | Neural relational inference for interacting systems | https://scholar.google.com/scholar?cluster=5985084190905139950&hl=en&as_sdt=0,5 | 25 | 2,018 |
Nonconvex Optimization for Regression with Fairness Constraints | 103 | icml | 1 | 1 | 2023-06-17 02:59:33.644000 | https://github.com/jkomiyama/fairregresion | 4 | Nonconvex optimization for regression with fairness constraints | https://scholar.google.com/scholar?cluster=9324671354987177692&hl=en&as_sdt=0,22 | 3 | 2,018 |
Dynamic Evaluation of Neural Sequence Models | 130 | icml | 21 | 1 | 2023-06-17 02:59:33.858000 | https://github.com/benkrause/dynamic-evaluation | 102 | Dynamic evaluation of neural sequence models | https://scholar.google.com/scholar?cluster=7171182301432620931&hl=en&as_sdt=0,5 | 5 | 2,018 |
Semiparametric Contextual Bandits | 42 | icml | 11 | 1 | 2023-06-17 02:59:34.071000 | https://github.com/akshaykr/oracle_cb | 28 | Semiparametric contextual bandits | https://scholar.google.com/scholar?cluster=8044014700167945410&hl=en&as_sdt=0,5 | 6 | 2,018 |
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings | 187 | icml | 5 | 0 | 2023-06-17 02:59:34.285000 | https://github.com/aviralkumar2907/MMCE | 15 | Trainable calibration measures for neural networks from kernel mean embeddings | https://scholar.google.com/scholar?cluster=3110087003136366065&hl=en&as_sdt=0,5 | 5 | 2,018 |
Canonical Tensor Decomposition for Knowledge Base Completion | 318 | icml | 40 | 2 | 2023-06-17 02:59:34.504000 | https://github.com/facebookresearch/kbc | 241 | Canonical tensor decomposition for knowledge base completion | https://scholar.google.com/scholar?cluster=9542404017825528876&hl=en&as_sdt=0,36 | 60 | 2,018 |
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks | 576 | icml | 28 | 1 | 2023-06-17 02:59:34.719000 | https://github.com/brendenlake/SCAN | 155 | Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks | https://scholar.google.com/scholar?cluster=11276348225798571948&hl=en&as_sdt=0,5 | 9 | 2,018 |
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace | 332 | icml | 8 | 5 | 2023-06-17 02:59:34.934000 | https://github.com/yoonholee/MT-net | 35 | Gradient-based meta-learning with learned layerwise metric and subspace | https://scholar.google.com/scholar?cluster=16589702021969633682&hl=en&as_sdt=0,5 | 5 | 2,018 |
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling | 54 | icml | 3 | 0 | 2023-06-17 02:59:35.154000 | https://github.com/leekwoon/KR-DL-UCT | 31 | Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling | https://scholar.google.com/scholar?cluster=6730862284084733221&hl=en&as_sdt=0,5 | 4 | 2,018 |
Noise2Noise: Learning Image Restoration without Clean Data | 1,230 | icml | 301 | 5 | 2023-06-17 02:59:35.370000 | https://github.com/NVlabs/noise2noise | 1,284 | Noise2Noise: Learning image restoration without clean data | https://scholar.google.com/scholar?cluster=16764673643469433149&hl=en&as_sdt=0,5 | 44 | 2,018 |
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks | 67 | icml | 7 | 0 | 2023-06-17 02:59:35.584000 | https://github.com/LiQianxiao/discrete-MSA | 20 | An optimal control approach to deep learning and applications to discrete-weight neural networks | https://scholar.google.com/scholar?cluster=6252296046431903031&hl=en&as_sdt=0,33 | 5 | 2,018 |
Towards Binary-Valued Gates for Robust LSTM Training | 55 | icml | 11 | 2 | 2023-06-17 02:59:35.799000 | https://github.com/zhuohan123/g2-lstm | 74 | Towards binary-valued gates for robust lstm training | https://scholar.google.com/scholar?cluster=9655995199891931380&hl=en&as_sdt=0,41 | 2 | 2,018 |
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering | 89 | icml | 1 | 0 | 2023-06-17 02:59:36.013000 | https://github.com/lipan00123/IPM-for-submodular-hypergraphs | 0 | Submodular hypergraphs: p-laplacians, cheeger inequalities and spectral clustering | https://scholar.google.com/scholar?cluster=3565527307250795946&hl=en&as_sdt=0,33 | 1 | 2,018 |
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