Wei-Sheng Chin

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; #13,594 of 35,345 by trust here, #15,542 by citations
• Optimization & Training — 18 citations into it; #2,392 of 2,601 by trust here, #1,744 by citations
• Information Systems — 14 citations into it; #5,187 of 8,771 by trust here, #2,013 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #10,829 of 21,575 by trust here, #17,757 by citations
• Computational Mechanics — 7 citations into it; #4,549 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 Wei-Sheng Chin trusts overall

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

1. Hsiang-Fu Yu
2. Yehuda Koren
3. B. Recht
4. Chih-Jen Lin
5. Steffen Rendle
6. Cho-Jui Hsieh
7. Ryan T. McDonald
8. Martin A. Zinkevich
9. Robert M. Bell
10. S. Sathiya Keerthi

Who trusts Wei-Sheng Chin overall

1. Jakub Konecný
2. Huan Zhang
3. Yuchen Zhang
4. Ching-pei Lee
5. Cho-Jui Hsieh
6. Ilya Trofimov
7. Zhongqi Lu
8. Kumar Kshitij Patel
9. Bin Liu
10. Ting-Li Chen

Papers from Wei-Sheng Chin's perspective

Ordered by how strongly the researchers Wei-Sheng Chin 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
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