Yunqian Ma

Artificial Intelligence · 2004–2014

A researcher in the Science corpus: 6 papers; trusts 9 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #14,022 of 35,345 by trust here, #14,718 by citations
• Statistical Learning Theory — 15 citations into it
• Statistics and Probability — 13 citations into it; #1,444 of 12,932 by trust here, #2,108 by citations
• Computer Vision and Pattern Recognition — 11 citations into it; #4,036 of 21,575 by trust here, #7,249 by citations
• Control and Systems Engineering — 9 citations into it; #5,367 of 8,257 by trust here, #7,501 by citations
• Optimization & Training — 7 citations into it
• Scaling & Learning Theory — 6 citations into it

Whom Yunqian Ma trusts overall

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

1. Vladimir Naumovich Vapni
2. Bernhard Schölkopf
3. Alex Smola
4. Yuhai Wu
5. Robert A. Lordo
6. Trevor J. Hastie
7. Vladimir Naumovich Vapnik
8. Jerome H. Friedman
9. Robert Tibshirani
10. Shun-ichi Amari

Who trusts Yunqian Ma overall

1. Paulo Cortez
2. Octavio Loyola-González
3. Ergun Biçici
4. Felipe Antonio Chegury Viana
5. Ajay Kalra
6. Asaf Manela
7. Matteo Pastorino
8. Saman Razavi
9. S. Dey
10. Yuya Yoshikawa

Papers from Yunqian Ma's perspective

Ordered by how strongly the researchers Yunqian Ma 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. The Nature of Statistical Learning Theory
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Practical selection of SVM parameters and noise estimation for SVM regression (2004) — 2,016 citations
• Imbalanced Learning: Foundations, Algorithms, and Applications (2013) — 911 citations
• Support Vector Machines Applications (2014) — 167 citations
• Ensemble Machine Learning (2012) — 913 citations
• Ensemble Machine Learning: Methods and Applications (2012) — 877 citations
• Another look at statistical learning theory and regularization (2009) — 53 citations