Susan Zhang

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 6 papers; trusts 206 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 200 citations into it
• Computer Vision and Pattern Recognition — 72 citations into it
• Large Language Models — 65 citations into it; #722 of 799 by trust here, #203 by citations
• Natural Language Processing — 65 citations into it
• Optimization & Training — 35 citations into it
• Scaling & Learning Theory — 24 citations into it
• Model Architectures — 22 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Susan Zhang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Kaiming He
7. Yann LeCun
8. Quoc V. Le
9. Jimmy Ba
10. Jason Aaron Edward Weston

Who trusts Susan Zhang overall

1. Tim Dettmers
2. Luke S. Zettlemoyer
3. Weijia Shi
4. Jing Yu Koh
5. Elias Frantar
6. Irene Solaiman
7. Alex Troy Mallen
8. Zechun Liu
9. Michael Lewis
10. Saleh Ashkboos

Papers from Susan Zhang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Dota 2 with Large Scale Deep Reinforcement Learning (2019) — 2,179 citations
• A Theory on Adam Instability in Large-Scale Machine Learning (2023) — 56 citations
• Effective Theory of Transformers at Initialization (2023) — 20 citations
• Scaling Laws for Generative Mixed-Modal Language Models (2023) — 159 citations
• LIMA: Less Is More for Alignment (2023) — 1,362 citations
• OPT: Open Pre-trained Transformer Language Models (2022) — 4,844 citations