Ying Xiao

Artificial Intelligence · with DeCoste · 2012–2019

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #16,299 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #9,007 of 21,575 by trust here, #9,722 by citations
• Optimization & Training — 20 citations into it; #1,797 of 2,601 by trust here, #1,624 by citations
• Scaling & Learning Theory — 12 citations into it
• Statistical Learning Theory — 11 citations into it
• Numerical Methods & Scientific Computing — 8 citations into it
• Statistics and Probability — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it; #8,177 of 12,182 by trust here, #6,798 by citations

Whom Ying Xiao trusts overall

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

1. Chih-Jen Lin
2. Joel A. Tropp
3. W. Hoeffding
4. Karen Simonyan
5. Gene H. Golub
6. Sergey Ioffe
7. Yoshua Bengio
8. Geoffrey E. Hinton
9. Bernhard Schölkopf
10. Barak A. Pearlmutter

Who trusts Ying Xiao overall

1. Haim Avron
2. Alessandro Rudi
3. Simone Scardapane
4. Dmitry Storcheus
5. Namuk Park
6. Vitaly Feldman
7. David Lopez-Paz
8. Ilias Diakonikolas

Papers from Ying Xiao's perspective

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

Their papers

• An Investigation into Neural Net Optimization via Hessian Eigenvalue Density (2019) — 460 citations
• Compact Random Feature Maps (2013) — 88 citations
• Statistical algorithms and a lower bound for detecting planted cliques (2012) — 247 citations