Henry Zhu

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 68 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #10,055 of 35,345 by trust here, #5,682 by citations
• Reinforcement Learning — 90 citations into it; #920 of 2,242 by trust here, #582 by citations
• Control and Systems Engineering — 52 citations into it; #4,454 of 8,257 by trust here, #2,447 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Robotics & Control — 24 citations into it; #354 of 609 by trust here, #442 by citations
• Biomedical Engineering — 19 citations into it; #5,673 of 10,734 by trust here, #6,455 by citations
• Optimization & Training — 10 citations into it
• Aerospace Engineering — 8 citations into it

Whom Henry Zhu trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. Ronald J. Williams
4. John Schulman
5. Geoffrey E. Hinton
6. E. Todorov
7. Richard S. Sutton
8. Diederik P. Kingma
9. Jan Peters
10. Yoshua Bengio

Who trusts Henry Zhu overall

1. Sergey Levine
2. Anusha Nagabandi
3. Aravind Rajeswaran
4. Pieter Abbeel
5. Tao Chen
6. Alex X. Lee
7. Chelsea Finn
8. Abhishek Gupta
9. Kelvin Xu
10. Julian Ibarz

Papers from Henry Zhu's perspective

Ordered by how strongly the researchers Henry Zhu 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Soft Actor-Critic Algorithms and Applications (2018) — 3,200 citations
• Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost (2018) — 238 citations
• The Ingredients of Real-World Robotic Reinforcement Learning (2020) — 207 citations
• Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022) — 45 citations