Michelle A. Lee

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Control and Systems Engineering — 47 citations into it
• Reinforcement Learning — 43 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Biomedical Engineering — 21 citations into it
• Robotics & Control — 15 citations into it
• Optimization & Training — 6 citations into it

Whom Michelle A. Lee trusts overall

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

1. Oussama Khatib
2. Diederik P. Kingma
3. Sergey Levine
4. Geoffrey E. Hinton
5. Karen Simonyan
6. John Schulman
7. Pieter Abbeel
8. Alex Krizhevsky
9. Yoshua Bengio
10. Yann LeCun

Who trusts Michelle A. Lee overall

1. Xingyu Lin
2. Julian Ibarz
3. Andrew Jaegle
4. Yuke Zhu

Papers from Michelle A. Lee's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks (2018) — 456 citations
• Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks (2019) — 283 citations
• Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks (2019) — 238 citations