Gregory Kahn

Reinforcement Learning · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 239 researchers and is trusted by 107 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 121 citations into it; #372 of 2,242 by trust here, #474 by citations
• Artificial Intelligence — 106 citations into it; #2,406 of 35,345 by trust here, #4,231 by citations
• Computer Vision and Pattern Recognition — 82 citations into it; #13,013 of 21,575 by trust here, #11,721 by citations
• Control and Systems Engineering — 53 citations into it; #704 of 8,257 by trust here, #388 by citations
• Aerospace Engineering — 48 citations into it; #3,204 of 6,421 by trust here, #2,580 by citations
• Robotics & Control — 43 citations into it; #209 of 609 by trust here, #365 by citations
• Automotive Engineering — 26 citations into it; #2,240 of 3,081 by trust here, #1,602 by citations
• Biomedical Engineering — 19 citations into it; #743 of 10,734 by trust here, #2,574 by citations

Whom Gregory Kahn trusts overall

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

1. Sergey Levine
2. Diederik P. Kingma
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Pieter Abbeel
6. Alex Krizhevsky
7. Yoshua Bengio
8. Yann LeCun
9. Marc Peter Deisenroth
10. Jan Peters

Who trusts Gregory Kahn overall

1. Sergey Levine
2. Pieter Abbeel
3. Michael Janner
4. Aviral Kumar
5. Shixiang Shane Gu
6. Kevin Lu
7. Benjamin Eysenbach
8. Chelsea Finn
9. Ignasi Clavera
10. Igor Mordatch

Papers from Gregory Kahn's perspective

Ordered by how strongly the researchers Gregory Kahn 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning (2017) — 1,091 citations
• Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation (2017) — 315 citations
• PLATO: Policy learning using adaptive trajectory optimization (2016) — 140 citations
• Uncertainty-Aware Reinforcement Learning for Collision Avoidance (2017) — 337 citations
• Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation (2018) — 22 citations
• Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search (2015) — 447 citations
• BADGR: An Autonomous Self-Supervised Learning-Based Navigation System (2020) — 327 citations