Yiding Yang

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 253 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #17,504 of 35,345 by trust here, #21,878 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #9,434 of 21,575 by trust here, #11,083 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Graph Learning — 30 citations into it; #539 of 693 by trust here, #497 by citations
• Computational Mechanics — 19 citations into it; #7,645 of 10,001 by trust here, #4,600 by citations
• Model Architectures — 17 citations into it
• Statistical and Nonlinear Physics — 14 citations into it; #9,249 of 10,127 by trust here, #7,747 by citations
• Molecular Biology — 13 citations into it; #15,366 of 17,773 by trust here, #15,850 by citations

Whom Yiding Yang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Kaiming He
6. Yann LeCun
7. Jimmy Ba
8. Max Welling
9. Thomas Kipf
10. Ilya Sutskever

Who trusts Yiding Yang overall

1. Jingwen Ye
2. Dongze Lian
3. Xinchao Wang
4. Daquan Zhou
5. Xingyi Yang
6. Yongcheng Jing
7. Yanan Gu
8. Jianping Gou
9. Jiashi Feng
10. Mingli Song

Papers from Yiding Yang's perspective

Ordered by how strongly the researchers Yiding Yang 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Distilling Knowledge From Graph Convolutional Networks (2020) — 287 citations
• Factorizable Graph Convolutional Networks (2020) — 172 citations
• Amalgamating Knowledge from Heterogeneous Graph Neural Networks (2021) — 105 citations
• Overcoming Catastrophic Forgetting in Graph Neural Networks (2020) — 180 citations