Xingyu Lin

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 284 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #31,590 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 77 citations into it; #16,589 of 21,575 by trust here, #15,955 by citations
• Reinforcement Learning — 76 citations into it
• Control and Systems Engineering — 64 citations into it; #7,311 of 8,257 by trust here, #4,045 by citations
• Robotics & Control — 26 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Cognitive Neuroscience — 17 citations into it
• Computational Mechanics — 12 citations into it

Whom Xingyu Lin trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Sergey Levine
8. Kaiming He
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Xingyu Lin overall

1. Alan L. Yuille
2. Yu Zhang
3. Benjamin Eysenbach
4. Chenxi Liu
5. Siyuan Qiao
6. Lucio M. Dery
7. Youngmin Oh
8. Christopher Fifty
9. Yevgen Chebotar
10. Daniel Seita

Papers from Xingyu Lin's perspective

Ordered by how strongly the researchers Xingyu Lin 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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

• SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation (2020) — 291 citations
• Reinforcement Learning without Ground-Truth State (2019) — 27 citations
• Transfer of View-manifold Learning to Similarity Perception of Novel Objects (2017) — 13 citations
• GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators (2023) — 342 citations
• Adaptive Auxiliary Task Weighting for Reinforcement Learning (2019) — 133 citations