Yinhan Liu

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 8 papers; trusts 295 researchers and is trusted by 857 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 265 citations into it; #703 of 35,345 by trust here, #559 by citations
• Natural Language Processing — 257 citations into it; #117 of 5,688 by trust here, #42 by citations
• Computer Vision and Pattern Recognition — 105 citations into it; #4,919 of 21,575 by trust here, #3,191 by citations
• Evaluation & Benchmarks — 65 citations into it
• Large Language Models — 60 citations into it; #25 of 799 by trust here, #47 by citations
• Model Architectures — 28 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #1,832 of 3,145 by trust here, #572 by citations
• Optimization & Training — 22 citations into it

Whom Yinhan Liu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Jimmy Ba
7. Kaiming He
8. Quoc V. Le
9. Yann LeCun
10. Alex Graves

Who trusts Yinhan Liu overall

1. Mohit Bansal
2. Yi Tay
3. Michael Lewis
4. Zhe Gan
5. Luke S. Zettlemoyer
6. Li Dong
7. Percy Liang
8. Yejin Choi
9. Furu Wei
10. Jianfeng Gao

Papers from Yinhan Liu's perspective

Ordered by how strongly the researchers Yinhan Liu 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2019) — 13,006 citations
• SpanBERT: Improving Pre-training by Representing and Predicting Spans (2019) — 2,196 citations
• Mask-Predict: Parallel Decoding of Conditional Masked Language Models (2019) — 724 citations
• Recipes for Building an Open-Domain Chatbot (2020) — 1,145 citations
• Multilingual Denoising Pre-training for Neural Machine Translation (2020) — 2,142 citations
• Cloze-driven Pretraining of Self-attention Networks (2019) — 210 citations
• RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019) — 30,530 citations
• Constant-Time Machine Translation with Conditional Masked Language Models (2019) — 35 citations