Haochuan Li

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #9,711 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Optimization & Training — 29 citations into it; #1,038 of 2,601 by trust here, #599 by citations
• Safety, Robustness & Interpretability — 26 citations into it
• Scaling & Learning Theory — 26 citations into it
• Statistical Learning Theory — 17 citations into it
• Computational Mechanics — 10 citations into it; #2,909 of 10,001 by trust here, #855 by citations
• Statistics and Probability — 7 citations into it

Whom Haochuan Li trusts overall

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

1. Simon Shaolei Du
2. Amit Daniely
3. Kaiming He
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Christian Szegedy
7. Yuanzhi Li
8. Yoshua Bengio
9. Roman Vershynin
10. Ian J. Goodfellow

Who trusts Haochuan Li overall

1. Simon Shaolei Du
2. Difan Zou
3. Zeyuan Allen-Zhu
4. James Martens
5. Quanquan Gu
6. Yuan Cao
7. Yuanzhi Li
8. Samet Oymak
9. Sanjeev Arora
10. Cong Fang

Papers from Haochuan Li's perspective

Ordered by how strongly the researchers Haochuan 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. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
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

• Gradient Descent Finds Global Minima of Deep Neural Networks (2018) — 1,268 citations
• Convergence of Adversarial Training in Overparametrized Neural Networks (2019) — 119 citations