Christopher Liaw

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #30,564 of 35,345 by trust here, #17,776 by citations
• Scaling & Learning Theory — 8 citations into it; #208 of 428 by trust here, #218 by citations
• Statistical Learning Theory — 5 citations into it

Whom Christopher Liaw trusts overall

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

1. Matus Telgarsky
2. Peter L. Bartlett
3. Nadav Cohen
4. Dmitry Yarotsky
5. Anselm Blumer
6. Martin H. Anthony
7. Vladimir Naumovich Vapnik

Who trusts Christopher Liaw overall

1. Behnam Neyshabur
2. Zhiwei Jia
3. Max H. Farrell
4. Tengyuan Liang
5. Jianfeng Lu
6. Zhou Lu
7. Srinadh Bhojanapalli
8. Atsushi Nitanda

Papers from Christopher Liaw's perspective

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

1. On the Expressive Power of Deep Learning: A Tensor Analysis
2. Deep Learning
3. The Power of Depth for Feedforward Neural Networks
4. Benefits of Depth in Neural Networks
5. Approximation capabilities of multilayer feedforward networks
6. Error bounds for approximations with deep ReLU networks
7. Learnability and the Vapnik-Chervonenkis dimension
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks
10. ImageNet classification with deep convolutional neural networks

Their papers

• Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks (2017) — 548 citations
• Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes (2018) — 68 citations
• Nearly-tight VC-dimension bounds for piecewise linear neural networks (2017) — 190 citations