Haishan Ye

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 64 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #15,780 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #11,795 of 21,575 by trust here, #14,822 by citations
• Optimization & Training — 30 citations into it; #1,818 of 2,601 by trust here, #2,313 by citations
• Computational Mechanics — 23 citations into it; #7,879 of 10,001 by trust here, #8,947 by citations
• Model Architectures — 11 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Neural Architecture Search & AutoML — 7 citations into it; #155 of 283 by trust here, #217 by citations
• Statistics and Probability — 7 citations into it; #10,362 of 12,932 by trust here, #10,855 by citations

Whom Haishan Ye trusts overall

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

1. Alex Krizhevsky
2. Zeyuan Allen-Zhu
3. Geoffrey E. Hinton
4. Kaiming He
5. Yurii E. Nesterov
6. Francis R. Bach
7. Yoshua Bengio
8. Aaron Defazio
9. Karen Simonyan
10. Yann LeCun

Who trusts Haishan Ye overall

1. Chaoyang He
2. Xiang Li
3. Murali Annavaram
4. S. Meng
5. Andre Milzarek
6. Shusen Wang
7. Quoc Tran-Dinh
8. Sharan Vaswani
9. Yonggan Fu
10. Xiantao Xiao

Papers from Haishan Ye's perspective

Ordered by how strongly the researchers Haishan Ye 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. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation (2020) — 146 citations
• Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems (2020) — 145 citations
• Approximate Newton Methods and Their Local Convergence (2017) — 26 citations