Nathan Scales

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 7 papers; trusts 39 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 249 citations into it; #21,120 of 35,345 by trust here, #2,251 by citations
• Natural Language Processing — 117 citations into it
• Prompting & Reasoning — 76 citations into it; #247 of 359 by trust here, #99 by citations
• Computer Vision and Pattern Recognition — 75 citations into it

Whom Nathan Scales trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Graves
5. Ilya Sutskever
6. Alex Krizhevsky
7. Sepp Hochreiter
8. Yann LeCun
9. Jason Wei
10. Kaiming He

Who trusts Nathan Scales overall

1. Xinyun Chen
2. Jie Huang
3. Jason Wei

Papers from Nathan Scales's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Least-to-Most Prompting Enables Complex Reasoning in Large Language Models (2022) — 1,834 citations
• Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them (2022) — 2,044 citations
• Measuring Compositional Generalization: A Comprehensive Method on Realistic Data (2019) — 421 citations
• Large language models encode clinical knowledge (2022) — 4,703 citations
• Compositional Semantic Parsing with Large Language Models (2022) — 113 citations
• Large Language Models Can Be Easily Distracted by Irrelevant Context (2023) — 1,096 citations
• When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations