Xiang Lisa Li
Artificial Intelligence · 2021–2023
A researcher in the Science corpus: 6 papers; trusts 279 researchers and is trusted by 290 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 162 citations into it; #1,191 of 35,345 by trust here, #1,242 by citations
- Natural Language Processing — 91 citations into it; #442 of 5,688 by trust here, #655 by citations
- Computer Vision and Pattern Recognition — 74 citations into it
- Large Language Models — 54 citations into it
- Prompting & Reasoning — 31 citations into it
- Evaluation & Benchmarks — 23 citations into it
- Generative Models — 21 citations into it
- Model Architectures — 19 citations into it
Whom Xiang Lisa Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alec Radford
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Chin-Yew Lin
- Alex Graves
Who trusts Xiang Lisa Li overall
- Boshi Wang
- Jordan Clive
- Xiao Liu
- Hyojin Bahng
- Jaemin Cho
- Chunting Zhou
- Barret Zoph
- Yi-Lin Sung
- Weizhe Yuan
- Luke S. Zettlemoyer
Papers from Xiang Lisa Li's perspective
Ordered by how strongly the researchers Xiang Lisa Li trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Prefix-Tuning: Optimizing Continuous Prompts for Generation (2021) — 6,083 citations
- Diffusion-LM Improves Controllable Text Generation (2022) — 1,430 citations
- Contrastive Decoding: Open-ended Text Generation as Optimization (2022) — 688 citations
- Learning to Compress Prompts with Gist Tokens (2023) — 409 citations
- Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2022) — 389 citations