Mingrui Wu

Artificial Intelligence · 2006–2011

A researcher in the Science corpus: 4 papers; trusts 40 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #9,875 of 35,345 by trust here, #22,463 by citations
• Statistical Learning Theory — 12 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it; #4,681 of 21,575 by trust here, #9,872 by citations
• Information Systems — 10 citations into it; #1,386 of 8,771 by trust here, #4,012 by citations
• Optimization & Training — 10 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Statistics and Probability — 8 citations into it
• Information Retrieval & Search — 6 citations into it

Whom Mingrui Wu trusts overall

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

1. L. Eon Bottou
2. John Shawe-Taylor
3. Andrew Y. Ng
4. Jerome H. Friedman
5. Stella X. Yu
6. Hang Li
7. Alexander Strehl
8. Thorsten Joachims
9. Vladimir Naumovich Vapnik
10. John Scott Bridle

Who trusts Mingrui Wu overall

1. Yu Zhang
2. Yi Yang
3. Feiping Nie
4. Saúl Solorio-Fernández
5. Zheng Wang
6. Hossein Mobahi
7. Dong Xu
8. Yangqiu Song
9. Kwang Moo Yi
10. Elad Hazan

Papers from Mingrui Wu's perspective

Ordered by how strongly the researchers Mingrui Wu 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. 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

• Gradient descent optimization of smoothed information retrieval metrics (2010) — 214 citations
• A Local Learning Approach for Clustering (2006) — 301 citations
• Supervised probabilistic principal component analysis (2006) — 182 citations
• Hierarchical Classification via Orthogonal Transfer (2011) — 106 citations