Matthew Lai

Artificial Intelligence · 2015–2024

A researcher in the Science corpus: 7 papers; trusts 49 researchers and is trusted by 282 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #3,178 of 35,345 by trust here, #750 by citations
• Reinforcement Learning — 128 citations into it; #1,001 of 2,242 by trust here, #90 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #2,442 of 21,575 by trust here, #1,942 by citations
• Game & Decision Theory — 63 citations into it
• Economics and Econometrics — 50 citations into it; #11,585 of 23,481 by trust here, #16,755 by citations
• Management Science and Operations Research — 39 citations into it
• Optimization & Training — 19 citations into it
• Model Architectures — 15 citations into it

Whom Matthew Lai trusts overall

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

1. Yurii E. Nesterov
2. Matthew D. Zeiler
3. Sebastian Thrun
4. L. Victor Allis
5. Shirish Chinchalkar
6. Geoffrey E. Hinton
7. Ilya Sutskever
8. Claude E. Shannon
9. Jonathan Baxter
10. Kunihito Hoki

Who trusts Matthew Lai overall

1. Yuxi Li
2. Briland Hitaj
3. David Silver
4. Yedid Hoshen
5. Charu C. Aggarwal
6. Peter W. Battaglia
7. Sjoerd van Steenkiste
8. J. Urgen Schmidhuber
9. Alvaro Sanchez-Gonzalez
10. David Raposo

Papers from Matthew Lai's perspective

Ordered by how strongly the researchers Matthew Lai 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Mastering the game of Go without human knowledge (2017) — 10,580 citations
• Interaction Networks for Learning about Objects, Relations and Physics (2016) — 1,568 citations
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018) — 4,131 citations
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017) — 2,110 citations
• Giraffe: Using Deep Reinforcement Learning to Play Chess (2015) — 115 citations
• Genie: Generative Interactive Environments (2024) — 696 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations