Zhize Li

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it; #30,785 of 35,345 by trust here, #25,809 by citations
• Optimization & Training — 27 citations into it
• Computational Mechanics — 12 citations into it; #8,766 of 10,001 by trust here, #6,582 by citations
• Statistical Learning Theory — 10 citations into it; #945 of 1,038 by trust here, #893 by citations
• Federated & Distributed Learning — 9 citations into it

Whom Zhize Li trusts overall

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

1. Yurii E. Nesterov
2. Zeyuan Allen-Zhu
3. Samuel Horváth
4. Peter Richtárik
5. Diederik P. Kingma
6. Dmitry Kovalev
7. Filip Hanzely
8. Sebastian U. Stich
9. Surbhi Goel
10. Jimmy Ba

Who trusts Zhize Li overall

1. Constantin Philippenko
2. Farzin Haddadpour
3. Jihong Park
4. Durmus Alp Emre Acar
5. Aymeric Dieuleveut
6. Chaoyang He
7. Yuanzhi Li
8. Aryan Mokhtari
9. Alireza Fallah
10. Mohammad Mahdi Kamani

Papers from Zhize Li's perspective

Ordered by how strongly the researchers Zhize 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
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

• Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization (2020) — 158 citations
• Learning Two-layer Neural Networks with Symmetric Inputs (2018) — 62 citations