Ming Tan

Information Systems · with Dara · 2015

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 19 citations into it; #6,420 of 8,771 by trust here, #6,195 by citations
• Software — 14 citations into it; #890 of 1,227 by trust here, #839 by citations
• Artificial Intelligence — 10 citations into it
• Computer Networks and Communications — 7 citations into it; #7,460 of 9,886 by trust here, #6,788 by citations

Whom Ming Tan trusts overall

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

1. Jerome H. Friedman
2. Nick Littlestone
3. Nitesh V. Chawla
4. Eibe Frank
5. Thomas Zimmermann
6. Mark A. Hall
7. Shuo Wang
8. Christopher G. Atkeson
9. Martin J. Shepperd
10. David William Aha

Who trusts Ming Tan overall

1. Chakkrit Kla Tantithamthavorn
2. Thibaud Lutellier
3. Ahmed E. Hassan
4. Ken-ichi Matsumoto

Papers from Ming Tan's perspective

Ordered by how strongly the researchers Ming Tan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Special Invited Paper-Additive logistic regression: A statistical view of boosting
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. ImageNet classification with deep convolutional neural networks
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. SMOTE: Synthetic Minority Over-sampling Technique
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Locally Weighted Learning

Their papers

• Online Defect Prediction for Imbalanced Data (2015) — 301 citations