Thomas Liao

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 3 papers; trusts 504 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 185 citations into it
• Safety, Robustness & Interpretability — 76 citations into it
• Evaluation & Benchmarks — 73 citations into it; #1,720 of 3,147 by trust here, #2,938 by citations
• Natural Language Processing — 68 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it
• Safety Research — 24 citations into it
• Information Systems — 21 citations into it
• Sociology and Political Science — 13 citations into it

Whom Thomas Liao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Jia Deng
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. Matt Post

Who trusts Thomas Liao overall

1. Inioluwa Deborah Raji
2. Yacine Jernite
3. Indra Elizabeth Kumar
4. Sebastian Gehrmann
5. Aaron Horowitz
6. Tim Dettmers
7. Ben Hutchinson
8. Andrew D. Selbst
9. Yonatan Bitton

Papers from Thomas Liao's perspective

Ordered by how strongly the researchers Thomas Liao 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• The Capacity for Moral Self-Correction in Large Language Models (2023) — 214 citations
• Towards Measuring the Representation of Subjective Global Opinions in Language Models (2023) — 451 citations
• Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning (2021) — 158 citations