Siddharth Reddy

Reinforcement Learning · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 65 citations into it; #1,492 of 2,242 by trust here, #1,932 by citations
• Artificial Intelligence — 58 citations into it; #21,141 of 35,345 by trust here, #25,809 by citations
• Control and Systems Engineering — 20 citations into it; #4,420 of 8,257 by trust here, #4,962 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Management Science and Operations Research — 8 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Optimization & Training — 5 citations into it
• Robotics & Control — 5 citations into it

Whom Siddharth Reddy trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Dean A. Pomerleau
4. Martin A. Riedmiller
5. Richard S. Sutton
6. Brian D. Ziebart
7. Longxin Lin
8. David D. Lewis
9. Bilal Piot
10. J. Urgen Schmidhuber

Who trusts Siddharth Reddy overall

1. Gokul Swamy
2. Charles B. Schaff
3. Qingyang Wu
4. Anca Dragan
5. Matthew R. Walter
6. Matthew W. Hoffman
7. Lei Li
8. Dylan P. Losey
9. Sergey Levine
10. Andreea Bobu

Papers from Siddharth Reddy's perspective

Ordered by how strongly the researchers Siddharth Reddy 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Shared Autonomy via Deep Reinforcement Learning (2018) — 222 citations
• Learning Human Objectives by Evaluating Hypothetical Behavior (2019) — 83 citations
• SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards (2019) — 308 citations