Mingchen Li

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 136 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 142 citations into it; #28,456 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 65 citations into it
• Optimization & Training — 64 citations into it; #2,244 of 2,601 by trust here, #1,585 by citations
• Scaling & Learning Theory — 49 citations into it
• Statistical Learning Theory — 44 citations into it; #1,032 of 1,038 by trust here, #893 by citations
• Computational Mechanics — 26 citations into it; #9,740 of 10,001 by trust here, #7,173 by citations
• Statistics and Probability — 18 citations into it
• Statistical and Nonlinear Physics — 16 citations into it; #9,918 of 10,127 by trust here, #7,024 by citations

Whom Mingchen Li trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Simon Shaolei Du
4. Diederik P. Kingma
5. Yoshua Bengio
6. Yann LeCun
7. Peter L. Bartlett
8. Alex Graves
9. Kaiming He
10. Ilya Sutskever

Who trusts Mingchen Li overall

1. Tian Li
2. Shoaib Ahmed Siddiqui
3. Xiaobo Xia
4. Samet Oymak
5. Hwanjun Song
6. Shengyuan Hu
7. M. Soltanolkotabi
8. Tongliang Liu
9. Nitarshan Rajkumar

Papers from Mingchen Li's perspective

Ordered by how strongly the researchers Mingchen Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
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

• Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian (2019) — 108 citations
• Provable and Efficient Continual Representation Learning (2022) — 16 citations
• Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks (2019) — 396 citations