Alex Zhai

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 3 papers; trusts 480 researchers and is trusted by 95 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 45 citations into it; #3,524 of 21,575 by trust here, #6,485 by citations
• Artificial Intelligence — 34 citations into it; #13,490 of 35,345 by trust here, #9,170 by citations
• Representation & Self-Supervised Learning — 26 citations into it; #1,500 of 3,145 by trust here, #1,202 by citations
• Generative Models — 11 citations into it
• Model Architectures — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Media Technology — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Alex Zhai trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Ross B. Girshick
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Karen Simonyan
7. Yoshua Bengio
8. Ilya Sutskever
9. Yann LeCun
10. Andrew Zisserman

Who trusts Alex Zhai overall

1. Thomas Bonis
2. Xiao Fang
3. Yuta Koike
4. Chengxu Zhuang
5. Shalom Eliahou
6. Xiao Wang
7. Kaiming He
8. Zeyuan Allen-Zhu
9. Nanxuan Zhao
10. A. Piergiovanni

Papers from Alex Zhai's perspective

Ordered by how strongly the researchers Alex Zhai 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Local Aggregation for Unsupervised Learning of Visual Embeddings (2019) — 465 citations
• A high-dimensional CLT in W2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {W}_2$$\end{document} distance wi (2017) — 37 citations
• Fibonacci-like growth of numerical semigroups of a given genus (2011) — 84 citations