Yu-Chin Juan

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; #13,592 of 35,345 by trust here, #15,542 by citations
• Optimization & Training — 18 citations into it; #2,390 of 2,601 by trust here, #1,744 by citations
• Information Systems — 14 citations into it; #5,184 of 8,771 by trust here, #2,013 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #10,827 of 21,575 by trust here, #17,757 by citations
• Computational Mechanics — 7 citations into it; #4,547 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 Yu-Chin Juan trusts overall

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

1. Steffen Rendle
2. B. Recht
3. John C. Duchi
4. Chih-Jen Lin
5. Rong-En Fan
6. Cho-Jui Hsieh
7. Yehuda Koren
8. Matthew Richardson
9. Hsiang-Fu Yu
10. Elad Hazan

Who trusts Yu-Chin Juan overall

1. Bin Liu
2. Tongwen Huang
3. Jakub Konecný
4. Aurick Qiao
5. Junwei Pan
6. Xiangnan He
7. Yuchen Zhang
8. Ching-pei Lee
9. Zhiqi Zhang
10. Bryon Aragam

Papers from Yu-Chin Juan's perspective

Ordered by how strongly the researchers Yu-Chin Juan 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. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
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