Kavosh Asadi

Reinforcement Learning · 2015–2019

A researcher in the Science corpus: 7 papers; trusts 171 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 142 citations into it; #480 of 2,242 by trust here, #805 by citations
• Artificial Intelligence — 119 citations into it; #6,766 of 35,345 by trust here, #9,170 by citations
• Natural Language Processing — 25 citations into it; #2,023 of 5,688 by trust here, #2,660 by citations
• Management Science and Operations Research — 22 citations into it; #4,630 of 11,946 by trust here, #9,383 by citations
• Statistical Learning Theory — 19 citations into it
• Control and Systems Engineering — 18 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it

Whom Kavosh Asadi trusts overall

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

1. Richard S. Sutton
2. Satinder Singh
3. Andrew Barto
4. Ronald J. Williams
5. Dimitri P. Bertsekas
6. Michael L. Littman
7. Csaba Szepesvari
8. Diederik P. Kingma
9. Geoffrey E. Hinton
10. David Silver

Who trusts Kavosh Asadi overall

1. Ofir Nachum
2. Bo Dai
3. Anusha Nagabandi
4. Adam Gleave
5. Shangtong Zhang
6. Michael Janner
7. Ben M. Hambly
8. Gergely Neu
9. Yingdong Lu
10. Mohammad Norouzi

Papers from Kavosh Asadi's perspective

Ordered by how strongly the researchers Kavosh Asadi 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. Learning Multiple Layers of Features from Tiny Images
9. Reinforcement Learning: An Introduction
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Lipschitz Continuity in Model-based Reinforcement Learning (2018) — 196 citations
• Combating the Compounding-Error Problem with a Multi-step Model (2019) — 71 citations
• Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning (2017) — 339 citations
• A New Softmax Operator for Reinforcement Learning (2016) — 10 citations
• An Alternative Softmax Operator for Reinforcement Learning (2016) — 237 citations
• Sample-efficient Deep Reinforcement Learning for Dialog Control (2016) — 21 citations
• Strengths, Weaknesses, and Combinations of Model-based and Model-free Reinforcement Learning (2015) — 8 citations