Yuan Li

Artificial Intelligence · with Cohn · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 37 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #28,926 of 35,345 by trust here, #23,731 by citations
• Natural Language Processing — 17 citations into it; #2,421 of 5,688 by trust here, #4,032 by citations
• Reinforcement Learning — 11 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Yuan Li trusts overall

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

1. Yoon Kim
2. Yoshua Bengio
3. E. Tjong Kim Sang
4. Geoffrey E. Hinton
5. Guillaume Lample
6. Burr Settles
7. Jimmy Ba
8. Diederik P. Kingma
9. Ilya Sutskever
10. Meng Fang

Who trusts Yuan Li overall

1. Jonas Pfeiffer
2. Ivan Vulic
3. Sebastian Ruder
4. Subhabrata Mukherjee
5. Brielen Madureira
6. Jinlan Fu
7. Zewen Chi
8. Anne Lauscher
9. Phillip Rust
10. Michael Desmond

Papers from Yuan Li's perspective

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

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

Their papers

• Massively Multilingual Transfer for NER (2019) — 237 citations
• Learning how to Active Learn: A Deep Reinforcement Learning Approach (2017) — 313 citations