Alex X. Lee

Computer Vision and Pattern Recognition · 2013–2019

A researcher in the Science corpus: 8 papers; trusts 534 researchers and is trusted by 532 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 146 citations into it; #1,917 of 21,575 by trust here, #3,280 by citations
• Artificial Intelligence — 112 citations into it; #1,928 of 35,345 by trust here, #3,582 by citations
• Reinforcement Learning — 95 citations into it; #1,040 of 2,242 by trust here, #850 by citations
• Control and Systems Engineering — 61 citations into it; #614 of 8,257 by trust here, #216 by citations
• Generative Models — 46 citations into it; #1,245 of 1,956 by trust here, #832 by citations
• Robotics & Control — 30 citations into it; #257 of 609 by trust here, #161 by citations
• Aerospace Engineering — 20 citations into it
• Biomedical Engineering — 20 citations into it; #10,454 of 10,734 by trust here, #4,866 by citations

Whom Alex X. Lee trusts overall

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

1. Diederik P. Kingma
2. Sergey Levine
3. Geoffrey E. Hinton
4. Chelsea Finn
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. J. Urgen Schmidhuber
9. Jimmy Ba
10. Kaiming He

Who trusts Alex X. Lee overall

1. Chelsea Finn
2. Sergey Levine
3. Annie Xie
4. Frederik D. Ebert
5. Danijar Hafner
6. Suraj Nair
7. Karl Pertsch
8. Oleh Rybkin
9. Tianhe Yu
10. Sudeep Dasari

Papers from Alex X. Lee's perspective

Ordered by how strongly the researchers Alex X. Lee 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Self-Supervised Visual Planning with Temporal Skip Connections (2017) — 362 citations
• Stochastic Adversarial Video Prediction (2018) — 479 citations
• Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control (2018) — 474 citations
• Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model (2019) — 428 citations
• Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization (2013) — 562 citations
• Tracking deformable objects with point clouds (2013) — 195 citations
• Motion planning with sequential convex optimization and convex collision checking (2014) — 1,002 citations
• Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning (2018) — 67 citations