Yuan Cao

Artificial Intelligence · with Gu · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 218 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 123 citations into it; #4,459 of 35,345 by trust here, #5,424 by citations
• Optimization & Training — 63 citations into it; #527 of 2,601 by trust here, #552 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #7,592 of 21,575 by trust here, #8,296 by citations
• Scaling & Learning Theory — 56 citations into it
• Statistical Learning Theory — 37 citations into it; #565 of 1,038 by trust here, #602 by citations
• Model Architectures — 26 citations into it
• Computational Mechanics — 25 citations into it; #7,986 of 10,001 by trust here, #4,039 by citations
• Statistical and Nonlinear Physics — 15 citations into it

Whom Yuan Cao trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Simon Shaolei Du
5. Yuanzhi Li
6. Yoshua Bengio
7. Diederik P. Kingma
8. Zeyuan Allen-Zhu
9. Ali Rahimi
10. Yann LeCun

Who trusts Yuan Cao overall

1. Yuanzhi Li
2. Zeyuan Allen-Zhu
3. Quanquan Gu
4. Guangda Ji
5. Dongruo Zhou
6. Simon Shaolei Du
7. Soufiane Hayou
8. Ruiqi Gao
9. Boxi Wu
10. Mohammad Pezeshki

Papers from Yuan Cao's perspective

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

Their papers

• Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks (2019) — 435 citations
• Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks (2018) — 454 citations
• Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks (2018) — 222 citations
• Towards Understanding the Spectral Bias of Deep Learning (2019) — 313 citations