Zhi-Qin John Xu

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 90 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #8,329 of 35,345 by trust here, #13,163 by citations
• Scaling & Learning Theory — 39 citations into it; #127 of 428 by trust here, #253 by citations
• Optimization & Training — 21 citations into it; #894 of 2,601 by trust here, #1,225 by citations
• Statistical Learning Theory — 16 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Statistical and Nonlinear Physics — 13 citations into it; #8,488 of 10,127 by trust here, #3,979 by citations
• Computational Mechanics — 8 citations into it
• Safety, Robustness & Interpretability — 7 citations into it

Whom Zhi-Qin John Xu trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Yoshua Bengio
4. Jimmy Ba
5. Geoffrey E. Hinton
6. Moritz Hardt
7. Alex Krizhevsky
8. Sepp Hochreiter
9. Vladimir Naumovich Vapnik
10. George V. Cybenko

Who trusts Zhi-Qin John Xu overall

1. Yaoyu Zhang
2. Xu Cheng
3. Mohammad Pezeshki
4. Greg Yang
5. Ronen Basri
6. Yan Xiao
7. Ameya Dilip Jagtap
8. Tao Luo
9. Yuan Cao
10. Zhefan Rao

Papers from Zhi-Qin John Xu's perspective

Ordered by how strongly the researchers Zhi-Qin John Xu 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

• Training behavior of deep neural network in frequency domain (2018) — 413 citations
• Understanding training and generalization in deep learning by Fourier analysis (2018) — 115 citations
• Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks (2019) — 776 citations
• A type of generalization error induced by initialization in deep neural networks (2019) — 54 citations