Thomas W. Anthony

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #12,051 of 35,345 by trust here, #12,761 by citations
• Reinforcement Learning — 77 citations into it
• Game & Decision Theory — 59 citations into it
• Management Science and Operations Research — 50 citations into it
• Economics and Econometrics — 26 citations into it; #7,129 of 23,481 by trust here, #7,262 by citations
• Optimization & Training — 14 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Thomas W. Anthony trusts overall

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

1. Ronald J. Williams
2. Stéphane Ross
3. David Silver
4. Diederik P. Kingma
5. Levente Kocsis
6. Sylvain Gelly
7. Richard S. Sutton
8. Hal Daumé Iii
9. J. Bagnell
10. Jimmy Ba

Who trusts Thomas W. Anthony overall

1. Eric Zelikman
2. Hector Geffner
3. James Goodman
4. Iddo Drori
5. Yuhuai Wu
6. Sebastian Schulze
7. Paul Christiano
8. Ching-An Cheng
9. Wen Sun
10. Samuel Sokota

Papers from Thomas W. Anthony's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Their papers

• Thinking Fast and Slow with Deep Learning and Tree Search (2017) — 481 citations
• From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization (2020) — 107 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations