Yu Wang

Natural Language Processing · with Gao · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 116 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 160 citations into it; #2,839 of 5,688 by trust here, #679 by citations
• Artificial Intelligence — 147 citations into it; #13,394 of 35,345 by trust here, #2,584 by citations
• Evaluation & Benchmarks — 60 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it; #14,197 of 21,575 by trust here, #2,618 by citations
• Large Language Models — 27 citations into it; #336 of 799 by trust here, #239 by citations
• Optimization & Training — 9 citations into it

Whom Yu Wang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Jimmy Ba
5. Ilya Sutskever
6. Xiaodong Liu
7. Sepp Hochreiter
8. Alex Krizhevsky
9. Yann LeCun
10. Kaiming He

Who trusts Yu Wang overall

1. Xiaodong Liu
2. Wenhui Wang

Papers from Yu Wang's perspective

Ordered by how strongly the researchers Yu Wang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
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

• Unified Language Model Pre-training for Natural Language Understanding and Generation (2019) — 1,676 citations
• UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training (2020) — 428 citations
• The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020) — 62 citations
• Adversarial Training for Large Neural Language Models (2020) — 206 citations