Yu Zhang

Artificial Intelligence · with Han · 2022

A researcher in the Science corpus: 2 papers; trusts 127 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 152 citations into it; #34,611 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 100 citations into it
• Computer Vision and Pattern Recognition — 62 citations into it
• Large Language Models — 46 citations into it; #773 of 799 by trust here, #673 by citations
• Prompting & Reasoning — 42 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Yu Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Timo Schick
7. Christian Szegedy
8. Kaiming He
9. Yann LeCun
10. Richard Socher

Who trusts Yu Zhang overall

1. Jiawei Han

Papers from Yu Zhang's perspective

Ordered by how strongly the researchers Yu Zhang 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. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning (2022) — 65 citations
• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding (2022) — 302 citations