glenn. fung

Artificial Intelligence · 2001–2011

A researcher in the Science corpus: 8 papers; trusts 26 researchers and is trusted by 74 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #3,897 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #3,740 of 21,575 by trust here, #6,795 by citations
• Statistics and Probability — 11 citations into it; #5,080 of 12,932 by trust here, #6,552 by citations
• Evaluation & Benchmarks — 10 citations into it
• Optimization & Training — 10 citations into it
• Control and Systems Engineering — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Media Technology — 7 citations into it; #890 of 3,621 by trust here, #2,598 by citations

Whom glenn. fung trusts overall

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

1. Olvi L. Mangasarian
2. Yuh-Jye Lee
3. Vladimir Naumovich Vapni
4. Robert A. Lordo
5. Johan A. K. Suykens
6. Sanjoy Dasgupta
7. Rohan Shah
8. Kristin P. Bennett
9. Paul S. Bradley
10. Robert Tibshirani

Who trusts glenn. fung overall

1. Emilio Carrizosa
2. Hwanjo Yu
3. Dolores Romero Morales
4. Jieping Ye
5. Nahla H. Barakat
6. Jaideep Vaidya
7. Xin-jun Peng
8. Hao Helen Zhang
9. Guo-Xun Yuan
10. S. Sathiya Keerthi

Papers from glenn. fung's perspective

Ordered by how strongly the researchers glenn. fung 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. The Nature of Statistical Learning Theory
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

• Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches (2007) — 424 citations
• Proximal support vector machine classifiers (2001) — 918 citations
• Structure learning in random fields for heart motion abnormality detection (2008) — 145 citations
• Rule extraction from linear support vector machines (2005) — 171 citations
• Convex Principal Feature Selection (2010) — 55 citations
• Active Learning from Crowds (2011) — 354 citations
• Multicategory Proximal Support Vector Machine Classifiers (2005) — 339 citations
• A Feature Selection Newton Method for Support Vector Machine Classification (2004) — 321 citations