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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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