Difan Zou

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 110 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 139 citations into it; #8,888 of 35,345 by trust here, #7,172 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #16,582 of 21,575 by trust here, #15,162 by citations
• Optimization & Training — 45 citations into it; #1,072 of 2,601 by trust here, #804 by citations
• Safety, Robustness & Interpretability — 39 citations into it
• Scaling & Learning Theory — 35 citations into it
• Model Architectures — 23 citations into it
• Computational Mechanics — 17 citations into it
• Statistical Learning Theory — 17 citations into it; #889 of 1,038 by trust here, #770 by citations

Whom Difan Zou trusts overall

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

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

Who trusts Difan Zou overall

1. Quanquan Gu
2. Zeyuan Allen-Zhu
3. Yuan Cao
4. Yuanzhi Li
5. Simon Shaolei Du
6. Samet Oymak
7. Greg Yang
8. Sanjeev Arora
9. Yuanbin Cao
10. Cong Fang

Papers from Difan Zou's perspective

Ordered by how strongly the researchers Difan Zou 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks (2018) — 454 citations
• Gradient descent optimizes over-parameterized deep ReLU networks (2019) — 290 citations
• Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks (2019) — 339 citations
• Improving Adversarial Robustness Requires Revisiting Misclassified Examples (2020) — 849 citations
• An Improved Analysis of Training Over-parameterized Deep Neural Networks (2019) — 260 citations