Nisan Stiennon

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #23,420 of 35,345 by trust here, #4,772 by citations
• Natural Language Processing — 73 citations into it; #4,776 of 5,688 by trust here, #1,130 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Large Language Models — 13 citations into it; #459 of 799 by trust here, #364 by citations
• Optimization & Training — 8 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Nisan Stiennon trusts overall

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

1. Alec Radford
2. Diederik P. Kingma
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Alex Krizhevsky
7. Jimmy Ba
8. Thorsten Joachims
9. John Schulman
10. Yann LeCun

Who trusts Nisan Stiennon overall

1. J'er'emy Scheurer
2. Angelica Chen
3. Tomasz Korbak
4. Jon Ander Campos
5. Rafael Rafailov
6. Harrison Lee
7. Ethan Perez
8. Hanze Dong
9. Adam Gleave
10. Yu Meng

Papers from Nisan Stiennon's perspective

Ordered by how strongly the researchers Nisan Stiennon 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Fine-Tuning Language Models from Human Preferences (2019) — 2,638 citations
• Learning to summarize from human feedback (2020) — 3,335 citations
• Learning to summarize with human feedback (2020) — 121 citations