Yanmin Sun

Artificial Intelligence · 2006–2009

A researcher in the Science corpus: 3 papers; trusts 33 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #5,987 of 35,345 by trust here, #6,517 by citations
• Information Systems — 22 citations into it
• Statistical Learning Theory — 17 citations into it
• Optimization & Training — 15 citations into it
• Algorithms & Complexity — 12 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Yanmin Sun trusts overall

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

1. Robert E. Schapire
2. Jerome H. Friedman
3. Yoav Freund
4. Kai Ming Ting
5. Leo Breiman
6. Nitesh V. Chawla
7. J. Ross Quinlan
8. Bianca Zadrozny
9. John H. Holland
10. Mahesh V. Joshi

Who trusts Yanmin Sun overall

1. Victoria López
2. Alberto Fernández
3. Bartosz Krawczyk
4. Shuo Wang
5. Francisco Herrera
6. Mikel Galar
7. Paula Branco
8. Taghi M. Khoshgoftaar
9. Jingrui He
10. Haibo He

Papers from Yanmin Sun's perspective

Ordered by how strongly the researchers Yanmin Sun 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. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Cost-sensitive boosting for classification of imbalanced data (2007) — 1,536 citations
• Classification of Imbalanced Data: a Review (2009) — 1,588 citations
• Boosting for Learning Multiple Classes with Imbalanced Class Distribution (2006) — 322 citations