Xiaolong Wang

Artificial Intelligence · International Conference on Learning Representations · 2019–2023

A researcher in the Science corpus: 14 papers; trusts 1,208 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 422 citations into it; #22,344 of 35,345 by trust here, #8,525 by citations
• Computer Vision and Pattern Recognition — 343 citations into it; #21,039 of 21,575 by trust here, #9,435 by citations
• Reinforcement Learning — 215 citations into it
• Control and Systems Engineering — 120 citations into it; #4,827 of 8,257 by trust here, #1,294 by citations
• Representation & Self-Supervised Learning — 110 citations into it; #2,481 of 3,145 by trust here, #1,913 by citations
• Management Science and Operations Research — 41 citations into it
• Optimization & Training — 41 citations into it
• Robotics & Control — 39 citations into it

Whom Xiaolong Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Karen Simonyan

Who trusts Xiaolong Wang overall

1. Wei-Hong Li
2. Nicklas Hansen
3. Yonglong Tian
4. Yisheng Song
5. Xialei Liu
6. Carl Doersch
7. Ilija Radosavovic
8. Eleni Triantafillou
9. Renrui Zhang
10. Oscar Mañas

Papers from Xiaolong Wang's perspective

Ordered by how strongly the researchers Xiaolong Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• What Should Not Be Contrastive in Contrastive Learning (2020) — 342 citations
• Learning Continuous Image Representation with Local Implicit Image Function (2020) — 970 citations
• DexMV: Imitation Learning for Dexterous Manipulation from Human Videos (2021) — 328 citations
• Test-Time Training for Out-of-Distribution Generalization (2019) — 107 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Multi-Task Reinforcement Learning with Soft Modularization (2020) — 241 citations
• Hand-Object Contact Consistency Reasoning for Human Grasps Generation (2021) — 252 citations
• Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning (2020) — 467 citations
• Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning (2020) — 96 citations
• TD-MPC2: Scalable, Robust World Models for Continuous Control (2023) — 506 citations
• Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization (2021) — 67 citations
• Temporal Difference Learning for Model Predictive Control (2022) — 447 citations
• BeBold: Exploration Beyond the Boundary of Explored Regions (2020) — 45 citations
• A New Meta-Baseline for Few-Shot Learning (2020) — 224 citations