Hongyang Gao

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 372 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #5,959 of 35,345 by trust here, #7,797 by citations
• Graph Learning — 49 citations into it
• Computer Vision and Pattern Recognition — 47 citations into it; #6,265 of 21,575 by trust here, #9,872 by citations
• Model Architectures — 25 citations into it
• Statistical and Nonlinear Physics — 16 citations into it; #1,738 of 10,127 by trust here, #2,631 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Molecular Biology — 13 citations into it; #5,451 of 17,773 by trust here, #9,030 by citations
• Natural Language Processing — 9 citations into it

Whom Hongyang Gao trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Ilya Sutskever
8. Ross B. Girshick
9. Ruslan Salakhutdinov
10. Kyunghyun Cho

Who trusts Hongyang Gao overall

1. Hao Yuan
2. Shuiwang Ji
3. Kaixiong Zhou
4. Christopher Morris
5. Wengong Jin
6. Ekagra Ranjan
7. Fan Zhou
8. Lan Wei
9. Davide Bacciu
10. Anton Tsitsulin

Papers from Hongyang Gao's perspective

Ordered by how strongly the researchers Hongyang Gao 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Graph U-Nets (2019) — 1,309 citations
• Large-Scale Learnable Graph Convolutional Networks (2018) — 656 citations
• Towards Deeper Graph Neural Networks (2020) — 738 citations