Yian Zhang

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 37 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #32,552 of 35,345 by trust here, #5,638 by citations
• Natural Language Processing — 94 citations into it
• Evaluation & Benchmarks — 43 citations into it
• Large Language Models — 30 citations into it
• Safety, Robustness & Interpretability — 8 citations into it

Whom Yian Zhang trusts overall

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

1. Diederik P. Kingma
2. Allyson Ettinger
3. Geoffrey E. Hinton
4. Jacob Devlin
5. Alex Wang
6. Yoshua Bengio
7. Jimmy Ba
8. Ilya Sutskever
9. Alex Krizhevsky
10. Kyunghyun Cho

Who trusts Yian Zhang overall

1. Samuel R. Bowman
2. Phillip Rust
3. Clara Vania
4. Nikita Nangia
5. Yonatan Belinkov
6. Anna Rogers
7. Jason Wei
8. Jonas Pfeiffer

Papers from Yian Zhang's perspective

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

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

Their papers

• Holistic Evaluation of Language Models (2023) — 1,863 citations
• When Do You Need Billions of Words of Pretraining Data? (2020) — 164 citations
• Learning Helpful Inductive Biases from Self-Supervised Pretraining (2020) — 28 citations