Yong Zhuang

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #13,593 of 35,345 by trust here, #15,542 by citations
• Optimization & Training — 18 citations into it; #2,391 of 2,601 by trust here, #1,744 by citations
• Information Systems — 14 citations into it; #5,186 of 8,771 by trust here, #2,013 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #10,828 of 21,575 by trust here, #17,757 by citations
• Computational Mechanics — 7 citations into it; #4,548 of 10,001 by trust here, #7,173 by citations
• Management Science and Operations Research — 6 citations into it
• Recommender Systems — 6 citations into it

Whom Yong Zhuang trusts overall

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

1. Chih-Jen Lin
2. Steffen Rendle
3. Hsiang-Fu Yu
4. S. Sathiya Keerthi
5. Cho-Jui Hsieh
6. B. Recht
7. Tom Minka
8. Trond Steihaug
9. John C. Duchi
10. Yehuda Koren

Who trusts Yong Zhuang overall

1. Jakub Konecný
2. Yuchen Zhang
3. Ching-pei Lee
4. Ilya Trofimov
5. Zhongqi Lu
6. Ting-Li Chen
7. Jinoh Oh
8. Xiangru Lian
9. Hyokun Yun
10. Huan Zhang

Papers from Yong Zhuang's perspective

Ordered by how strongly the researchers Yong Zhuang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A fast parallel SGD for matrix factorization in shared memory systems (2013) — 167 citations
• Distributed Newton Methods for Regularized Logistic Regression (2015) — 46 citations
• Field-aware Factorization Machines for CTR Prediction (2016) — 797 citations
• A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems (2015) — 108 citations