Zhuohan Li

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 12 papers; trusts 111 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 280 citations into it
• Computer Vision and Pattern Recognition — 144 citations into it
• Efficiency & Compression — 117 citations into it; #1,147 of 1,264 by trust here, #204 by citations
• Natural Language Processing — 85 citations into it
• Large Language Models — 83 citations into it
• Optimization & Training — 58 citations into it
• Model Architectures — 55 citations into it
• Evaluation & Benchmarks — 50 citations into it

Whom Zhuohan Li trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Jimmy Ba
6. Kaiming He
7. Ilya Sutskever
8. Ashish Vaswani
9. Yann LeCun
10. Richard Socher

Who trusts Zhuohan Li overall

1. Derya Soydaner
2. Kalpesh Krishna
3. Yinmin Zhong
4. Orevaoghene Ahia
5. Gavin Kerrigan
6. Jonathan S. Rosenfeld
7. Andrei Ivanov
8. Xu Han
9. Vijay Anand Korthikanti
10. Dan Hendrycks

Papers from Zhuohan Li's perspective

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

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

Their papers

• Judging LLM-as-a-judge with MT-Bench and Chatbot Arena (2023) — 9,887 citations
• Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers (2020) — 157 citations
• LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset (2023) — 484 citations
• Efficient Memory Management for Large Language Model Serving with PagedAttention (2023) — 7,142 citations
• TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models (2021) — 166 citations
• Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View (2019) — 234 citations
• High-throughput Generative Inference of Large Language Models with a Single GPU (2023) — 797 citations
• Soil heavy metal pollution and food safety in China: Effects, sources and removing technology. (2020) — 751 citations
• Efficient Training of BERT by Progressively Stacking (2019) — 192 citations
• Hint-based Training for Non-Autoregressive Translation (2018) — 21 citations
• This paper is included in the Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation.
• AlpaServe: Statistical Multiplexing with Model Parallelism for Deep Learning Serving