Harris Chan

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 159 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it
• Computer Vision and Pattern Recognition — 149 citations into it
• Reinforcement Learning — 92 citations into it
• Optimization & Training — 74 citations into it
• Control and Systems Engineering — 27 citations into it
• Computational Mechanics — 26 citations into it
• Scaling & Learning Theory — 20 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Harris Chan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. L. Eon Bottou
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. James Martens
8. Kaiming He
9. Ilya Sutskever
10. Sergey Levine

Papers from Harris Chan's perspective

Ordered by how strongly the researchers Harris Chan 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Inner Monologue: Embodied Reasoning through Planning with Language Models (2022) — 1,446 citations
• Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models (2022) — 84 citations
• Large Language Models Are Human-Level Prompt Engineers (2022) — 1,479 citations
• Learning Domain Invariant Representations in Goal-conditioned Block MDPs (2021) — 19 citations
• An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise (2019) — 24 citations
• Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise (2019) — 27 citations