Richard Ngo

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 155 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it
• Safety, Robustness & Interpretability — 37 citations into it
• Reinforcement Learning — 34 citations into it
• Large Language Models — 16 citations into it
• Alignment & RLHF — 13 citations into it; #24 of 62 by trust here, #50 by citations
• Computational Theory and Mathematics — 12 citations into it
• Safety Research — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it; #12,540 of 21,575 by trust here, #15,955 by citations

Whom Richard Ngo trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Christian P. Robert
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. J. Urgen Schmidhuber
7. Diederik P. Kingma
8. Andrew Barto
9. Alex Krizhevsky
10. Paul Christiano

Who trusts Richard Ngo overall

1. Wes Gurnee
2. Collin Burns
3. Leo Gao
4. Leonard Bereska
5. Pavel Izmailov
6. Hoagy Cunningham
7. Nicholas Carlini
8. Tianhao Shen

Papers from Richard Ngo's perspective

Ordered by how strongly the researchers Richard Ngo 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The alignment problem from a deep learning perspective (2022) — 333 citations
• Avoiding Side Effects By Considering Future Tasks (2020) — 51 citations