Yuanbin Cao

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 72 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it; #32,840 of 35,345 by trust here, #16,166 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Optimization & Training — 39 citations into it; #1,136 of 2,601 by trust here, #1,509 by citations
• Scaling & Learning Theory — 38 citations into it
• Statistical Learning Theory — 23 citations into it; #524 of 1,038 by trust here, #569 by citations
• Computational Mechanics — 18 citations into it
• Model Architectures — 18 citations into it
• Statistical and Nonlinear Physics — 14 citations into it

Whom Yuanbin Cao trusts overall

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

1. Yuanzhi Li
2. Simon Shaolei Du
3. Zeyuan Allen-Zhu
4. Peter L. Bartlett
5. Sanjeev Arora
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Roman Vershynin
9. Matus Telgarsky
10. Kaiming He

Who trusts Yuanbin Cao overall

1. Gilad Yehudai
2. Yuan Cao
3. Zeyuan Allen-Zhu
4. Colin Wei
5. Quanquan Gu
6. Atsushi Nitanda
7. Ohad Shamir
8. Yuanzhi Li
9. Samet Oymak
10. E. Weinan

Papers from Yuanbin Cao's perspective

Ordered by how strongly the researchers Yuanbin Cao 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
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 optimizes over-parameterized deep ReLU networks (2019) — 290 citations
• Fully-Hierarchical Fine-Grained Prosody Modeling For Interpretable Speech Synthesis (2020) — 131 citations
• A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks (2019) — 71 citations