Shayegan Omidshafiei

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 57 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it; #15,954 of 35,345 by trust here, #9,170 by citations
• Reinforcement Learning — 232 citations into it; #998 of 2,242 by trust here, #795 by citations
• Game & Decision Theory — 105 citations into it
• Management Science and Operations Research — 105 citations into it
• Economics and Econometrics — 47 citations into it
• Computational Theory and Mathematics — 29 citations into it; #7,301 of 12,182 by trust here, #2,104 by citations
• Optimization & Training — 29 citations into it
• Computer Networks and Communications — 11 citations into it

Whom Shayegan Omidshafiei trusts overall

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

1. Richard S. Sutton
2. Michael H. Bowling
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Graves
6. Yoshua Bengio
7. Volodymyr Mnih
8. Andrew Barto
9. David Silver
10. J. Urgen Schmidhuber

Who trusts Shayegan Omidshafiei overall

1. Hanlin Tang
2. Gregory Palmer
3. Xueguang Lyu
4. Kaiqing Zhang
5. Chris Amato
6. Pablo Hernandez-Leal
7. Ce Zhang
8. Afshin Oroojlooyjadid
9. Karl Tuyls
10. Xueguang Lu

Papers from Shayegan Omidshafiei's perspective

Ordered by how strongly the researchers Shayegan Omidshafiei 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Reinforcement Learning: An Introduction
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (2017) — 570 citations
• α-Rank: Multi-Agent Evaluation by Evolution (2019) — 141 citations
• Learning Hierarchical Teaching Policies for Cooperative Agents (2019) — 33 citations
• From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization (2020) — 107 citations
• Learning to Teach in Cooperative Multiagent Reinforcement Learning (2018) — 145 citations
• Neural Replicator Dynamics (2019) — 25 citations
• From Motor Control to Team Play in Simulated Humanoid Football (2021) — 163 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations
• Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions (2017) — 34 citations
• Deep Decentralized Multi-task Multi-Agent RL under Partial Observability (2017) — 27 citations