Fan Wang

Artificial Intelligence · with Wu · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 72 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #18,622 of 35,345 by trust here, #12,580 by citations
• Natural Language Processing — 59 citations into it; #4,359 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #17,891 of 21,575 by trust here, #11,083 by citations
• Large Language Models — 19 citations into it
• Computational Chemistry & Drug Discovery — 17 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 8 citations into it

Whom Fan Wang trusts overall

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

1. Diederik P. Kingma
2. Oriol Vinyals
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Jimmy Ba
7. Kyunghyun Cho
8. Alec Radford
9. Alex Krizhevsky
10. Quoc V. Le

Who trusts Fan Wang overall

1. Wen Zheng
2. Yi-Ting Yeh

Papers from Fan Wang's perspective

Ordered by how strongly the researchers Fan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Learning to Select Knowledge for Response Generation in Dialog Systems (2019) — 210 citations
• PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2019) — 296 citations
• Geometry-enhanced molecular representation learning for property prediction (2021) — 648 citations
• PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2020) — 150 citations