Hsuan-Tien Lin

Artificial Intelligence · Machine-mediated learning · 2005–2016

A researcher in the Science corpus: 7 papers; trusts 49 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #6,498 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #5,240 of 21,575 by trust here, #12,992 by citations
• Reinforcement Learning — 10 citations into it
• Information Systems — 9 citations into it; #5,596 of 8,771 by trust here, #6,195 by citations
• Management Science and Operations Research — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Optimization & Training — 7 citations into it
• Control and Systems Engineering — 6 citations into it; #961 of 8,257 by trust here, #1,943 by citations

Whom Hsuan-Tien Lin trusts overall

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

1. Bernhard Schölkopf
2. Vladimir Naumovich Vapni
3. David Goldberg
4. Charles A. Micchelli
5. S. Sathiya Keerthi
6. Vladimir Naumovich Vapnik
7. Corinna Cortes
8. Peter L. Bartlett
9. Chih-Wei Hsu
10. Bernhard E. Boser

Who trusts Hsuan-Tien Lin overall

1. Abdulhamit Subasi
2. Cheng-Lung Huang
3. Jia Deng
4. Cees G. M. Snoek
5. Chih-Jen Lin
6. Mohammad Ali Ghorbani
7. Ravinesh C. Deo
8. Marcus Rohrbach
9. Di Zhang
10. Charles Peter Elkan

Papers from Hsuan-Tien Lin's perspective

Ordered by how strongly the researchers Hsuan-Tien Lin 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

• Multilabel Classification with Principal Label Space Transformation (2012) — 329 citations
• A note on Platt’s probabilistic outputs for support vector machines (2007) — 1,003 citations
• Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent (2007) — 21 citations
• Automatic Bridge Bidding Using Deep Reinforcement Learning (2016) — 46 citations
• An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes (2009) — 17 citations
• Ordinal Regression by Extended Binary Classification (2006) — 315 citations
• A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods (2005) — 701 citations