Shuohuan Wang
Artificial Intelligence · 2019–2021
A researcher in the Science corpus: 5 papers; trusts 38 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 171 citations into it; #26,624 of 35,345 by trust here, #4,627 by citations
- Natural Language Processing — 137 citations into it; #4,393 of 5,688 by trust here, #1,362 by citations
- Computer Vision and Pattern Recognition — 78 citations into it
- Large Language Models — 66 citations into it; #532 of 799 by trust here, #367 by citations
- Evaluation & Benchmarks — 57 citations into it
- Representation & Self-Supervised Learning — 23 citations into it
- Optimization & Training — 11 citations into it
Whom Shuohuan Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alec Radford
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Richard Socher
- Kaiming He
- Yann LeCun
Who trusts Shuohuan Wang overall
- Zhuang Liu
- Yu Sun
- Ruiyang Ren
- Junqiu Wei
- Emily M. Bender
- Wayne Xin Zhao
- Yi Mao
- Xu Han
- Xiaodong Liu
- Pengcheng He
Papers from Shuohuan Wang's perspective
Ordered by how strongly the researchers Shuohuan Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- 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
- ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora (2020) — 121 citations
- ERNIE: Enhanced Representation through Knowledge Integration (2019) — 1,025 citations
- ERNIE 2.0: A Continual Pre-training Framework for Language Understanding (2019) — 902 citations
- ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021) — 88 citations
- ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021) — 607 citations