Xiao Zhang

Artificial Intelligence · with Gu · 2018

A researcher in the Science corpus: 1 paper; trusts 20 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #34,697 of 35,345 by trust here, #23,077 by citations
• Optimization & Training — 18 citations into it; #2,575 of 2,601 by trust here, #1,942 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #21,203 of 21,575 by trust here, #15,162 by citations
• Scaling & Learning Theory — 14 citations into it
• Statistical Learning Theory — 13 citations into it; #1,033 of 1,038 by trust here, #770 by citations
• Computational Mechanics — 9 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Xiao Zhang trusts overall

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

1. Rong Ge
2. M. Soltanolkotabi
3. Simon Shaolei Du
4. Jason D. Lee
5. Alex Krizhevsky
6. Kenji Kawaguchi
7. Geoffrey E. Hinton
8. Moritz Hardt
9. Itay Safran
10. Yoshua Bengio

Who trusts Xiao Zhang overall

1. Difan Zou
2. Yuanzhi Li
3. Zeyuan Allen-Zhu
4. Simon Shaolei Du
5. Quanquan Gu
6. Atsushi Nitanda
7. Yuanbin Cao

Papers from Xiao Zhang's perspective

Ordered by how strongly the researchers Xiao Zhang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• Learning One-hidden-layer ReLU Networks via Gradient Descent (2018) — 140 citations