Chris Junchi Li

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 46 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #16,655 of 35,345 by trust here, #15,866 by citations
• Computational Mechanics — 37 citations into it; #4,432 of 10,001 by trust here, #5,813 by citations
• Optimization & Training — 36 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Statistics and Probability — 10 citations into it
• Management Science and Operations Research — 9 citations into it
• Mathematical Optimization — 7 citations into it
• Numerical Analysis — 5 citations into it

Whom Chris Junchi Li trusts overall

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

1. Yurii E. Nesterov
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Shai Shalev-Shwartz
6. Zeyuan Allen-Zhu
7. L. Eon Bottou
8. Chi Jin
9. Ilya Sutskever
10. Yair Carmon

Who trusts Chris Junchi Li overall

1. Rachel A. Ward
2. Ashok Cutkosky
3. Tianbao Yang
4. Xiaoxia Wu
5. Cyrille W. Combettes
6. Luo Luo
7. Umut Simsekli
8. Dylan J. Foster
9. Quoc Tran-Dinh
10. Sai Praneeth Karimireddy

Papers from Chris Junchi Li's perspective

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

Their papers

• On the diffusion approximation of nonconvex stochastic gradient descent (2017) — 157 citations
• SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator (2018) — 683 citations