Edward Lockhart

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 9 papers; trusts 56 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #6,789 of 35,345 by trust here, #975 by citations
• Reinforcement Learning — 144 citations into it
• Computer Vision and Pattern Recognition — 93 citations into it
• Game & Decision Theory — 62 citations into it
• Management Science and Operations Research — 49 citations into it
• Generative Models — 41 citations into it; #861 of 1,956 by trust here, #346 by citations
• Economics and Econometrics — 35 citations into it
• Optimization & Training — 22 citations into it

Whom Edward Lockhart trusts overall

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

1. Martin A. Zinkevich
2. Richard S. Sutton
3. Michael H. Bowling
4. Johannes Heinrich
5. Noam Brown
6. Michael Bradley Johanson
7. David Silver
8. Marc Lanctot
9. F. Clarke
10. Andrew Barto

Who trusts Edward Lockhart overall

1. Kaiqing Zhang
2. Pablo Hernandez-Leal
3. Julien Pérolat
4. Shayegan Omidshafiei
5. Ye Jia
6. Takuma Okamoto
7. Wei Ping
8. Yu Zhang
9. Zhuoran Yang
10. R´emi Munos

Papers from Edward Lockhart's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. GENERATIVE ADVERSARIAL NETS
10. Going deeper with convolutions

Their papers

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Parallel WaveNet: Fast High-Fidelity Speech Synthesis (2017) — 912 citations
• Mastering Atari, Go, chess and shogi by planning with a learned model (2019) — 2,607 citations
• Efficient Neural Audio Synthesis (2018) — 936 citations
• Relational Deep Reinforcement Learning (2018) — 245 citations
• Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent (2019) — 93 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations
• Consistent Generative Query Networks (2018) — 42 citations
• Deep reinforcement learning with relational inductive biases (2018) — 231 citations