Miao Xu

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #30,181 of 35,345 by trust here, #8,365 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 14 citations into it; #2,345 of 2,601 by trust here, #787 by citations
• Computational Mechanics — 13 citations into it
• Statistical Learning Theory — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Signal Processing — 6 citations into it

Whom Miao Xu trusts overall

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

1. E.J. Candes
2. B. Recht
3. Francis R. Bach
4. Chih-Jen Lin
5. Shuiwang Ji
6. Kim-Chuan Toh
7. Robert Tibshirani
8. Jian-Feng Cai
9. Raghunandan H. Keshavan
10. Geoffrey E. Hinton

Who trusts Miao Xu overall

1. Hsiang-Fu Yu
2. Nikhil S. Rao
3. Federico Monti
4. Yue Zhu
5. Michael M. Bronstein
6. Inderjit S. Dhillon
7. James T. Kwok
8. Hwanjun Song
9. Xavier Bresson
10. Zhiyuan Shi

Papers from Miao Xu's perspective

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

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

Their papers

• Co-teaching: Robust training of deep neural networks with extremely noisy labels (2018) — 2,654 citations
• Speedup Matrix Completion with Side Information: Application to Multi-Label Learning (2013) — 305 citations