Vincent Y. Zhao
Artificial Intelligence · 2021–2022
A researcher in the Science corpus: 8 papers; trusts 175 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 421 citations into it; #11,950 of 35,345 by trust here, #1,134 by citations
- Natural Language Processing — 261 citations into it
- Computer Vision and Pattern Recognition — 180 citations into it; #12,228 of 21,575 by trust here, #1,560 by citations
- Evaluation & Benchmarks — 100 citations into it
- Large Language Models — 100 citations into it; #409 of 799 by trust here, #134 by citations
- Information Retrieval & Search — 90 citations into it
- Prompting & Reasoning — 52 citations into it; #156 of 359 by trust here, #55 by citations
- Optimization & Training — 11 citations into it
Whom Vincent Y. Zhao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Noam Shazeer
- Kaiming He
- Yann LeCun
- Jacob Devlin
- Alex Graves
Who trusts Vincent Y. Zhao overall
- Hong-Liu Cao
- Swaroop Mishra
- Timo Schick
- Luyu Gao
- Wayne Xin Zhao
- Akari Asai
- Thomas Scialom
- John X. Morris
- J'er'emy Scheurer
- Sewon Min
Papers from Vincent Y. Zhao's perspective
Ordered by how strongly the researchers Vincent Y. Zhao trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Finetuned Language Models Are Zero-Shot Learners (2021) — 5,221 citations
- Scaling Instruction-Finetuned Language Models (2022) — 4,301 citations
- Dialog Inpainting: Turning Documents into Dialogs (2022) — 77 citations
- Large Dual Encoders Are Generalizable Retrievers (2021) — 644 citations
- Attributed Text Generation via Post-hoc Research and Revision (2022) — 27 citations
- RARR: Researching and Revising What Language Models Say, Using Language Models (2022) — 339 citations
- Promptagator: Few-shot Dense Retrieval From 8 Examples (2022) — 350 citations
- Mixture-of-Experts with Expert Choice Routing (2022) — 810 citations