Xiang Lorraine Li

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 6 papers; trusts 112 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #23,918 of 35,345 by trust here, #2,711 by citations
• Natural Language Processing — 115 citations into it; #4,000 of 5,688 by trust here, #2,307 by citations
• Computer Vision and Pattern Recognition — 55 citations into it
• Evaluation & Benchmarks — 36 citations into it
• Large Language Models — 33 citations into it
• Information Retrieval & Search — 20 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Graph Learning — 9 citations into it

Whom Xiang Lorraine Li trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Richard Socher
4. Ilya Sutskever
5. Antoine Bordes
6. Diederik P. Kingma
7. Christopher D. Manning
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Tomas Mikolov

Who trusts Xiang Lorraine Li overall

1. D. Gunning
2. Adyasha Maharana
3. Joshua Feldman
4. Hongming Zhang
5. Peifeng Wang
6. Antoine Bosselut
7. Yi Mao
8. Jian Guan
9. Ninareh Mehrabi
10. Arian Hosseini

Papers from Xiang Lorraine Li's perspective

Ordered by how strongly the researchers Xiang Lorraine 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
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

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Commonsense Knowledge Base Completion (2016) — 180 citations
• Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval (2020) — 246 citations
• Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization (2021) — 62 citations
• Faith and Fate: Limits of Transformers on Compositionality (2023) — 650 citations
• ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning (2020) — 68 citations