Kaushik Subramanian

Reinforcement Learning · 2011–2022

A researcher in the Science corpus: 6 papers; trusts 39 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 54 citations into it; #2,209 of 2,242 by trust here, #1,100 by citations
• Artificial Intelligence — 38 citations into it
• Control and Systems Engineering — 20 citations into it; #7,556 of 8,257 by trust here, #5,227 by citations
• Management Science and Operations Research — 9 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Robotics & Control — 7 citations into it
• Cognitive Neuroscience — 6 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Kaushik Subramanian trusts overall

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

1. Csaba Szepesvari
2. Richard S. Sutton
3. Peter Auer
4. Thomas G. Dietterich
5. Andreas Karlsson
6. Andrew Y. Ng
7. Ronald E. Parr
8. Pieter Abbeel
9. Richard Dearden
10. W. B. Knox

Who trusts Kaushik Subramanian overall

1. Rein Houthooft
2. Todd Hester
3. Michael L. Littman
4. Kavosh Asadi
5. Christian Daniel
6. Allan Zhou
7. Xi Chen
8. Huan Ling
9. James MacGlashan
10. Anis Najar

Papers from Kaushik Subramanian's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Reinforcement Learning: An Introduction
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Human-level control through deep reinforcement learning
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Outracing champion Gran Turismo drivers with deep reinforcement learning (2022) — 497 citations
• Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning (2018) — 277 citations
• Exploration from Demonstration for Interactive Reinforcement Learning (2016) — 91 citations
• Policy Shaping: Integrating Human Feedback with Reinforcement Learning (2013) — 484 citations
• Novel Interaction Strategies for Learning from Teleoperation (2012) — 27 citations
• Apprenticeship Learning About Multiple Intentions (2011) — 197 citations