Chenguang Wang

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 177 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #27,390 of 35,345 by trust here, #18,586 by citations
• Natural Language Processing — 73 citations into it
• Large Language Models — 41 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it; #16,804 of 21,575 by trust here, #17,258 by citations
• Model Architectures — 20 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Molecular Biology — 9 citations into it

Whom Chenguang Wang trusts overall

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

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

Who trusts Chenguang Wang overall

1. Shumin Kong
2. Xiao Liu
3. Felix Stahlberg
4. Xu Han
5. Tianyu Guo
6. Shan You
7. Yehui Tang
8. Aohan Zeng
9. Percy Liang

Papers from Chenguang Wang's perspective

Ordered by how strongly the researchers Chenguang 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. 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. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Language Models with Transformers (2019) — 140 citations
• Language Models are Open Knowledge Graphs (2020) — 149 citations
• DeepStruct: Pretraining of Language Models for Structure Prediction (2022) — 98 citations