Junying Li

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 2 papers; trusts 89 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 29 citations into it; #17,016 of 21,575 by trust here, #17,757 by citations
• Artificial Intelligence — 22 citations into it; #26,857 of 35,345 by trust here, #23,731 by citations
• Computational Theory and Mathematics — 15 citations into it; #9,275 of 12,182 by trust here, #6,165 by citations
• Materials Chemistry — 11 citations into it; #7,158 of 11,513 by trust here, #9,657 by citations
• Model Architectures — 11 citations into it
• Molecular Biology — 10 citations into it
• Computational Chemistry & Drug Discovery — 8 citations into it; #283 of 425 by trust here, #333 by citations
• Representation & Self-Supervised Learning — 7 citations into it

Whom Junying Li trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Ross B. Girshick
9. Max Welling
10. Sander Dieleman

Who trusts Junying Li overall

1. David W. Romero
2. Chengxuan Ying
3. Katsuhiko Ishiguro
4. Weihua Hu
5. Beranger Dumont
6. Mengying Sun
7. Tianle Cai
8. Mark Hoogendoorn
9. Shin-ichi Maeda
10. Zaccary Alperstein

Papers from Junying Li's perspective

Ordered by how strongly the researchers Junying Li 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Learning Graph-Level Representation for Drug Discovery (2017) — 125 citations
• Deep Rotation Equivariant Network (2017) — 61 citations