Michael Tao

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 187 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #27,043 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 44 citations into it; #16,581 of 21,575 by trust here, #15,529 by citations
• Optimization & Training — 30 citations into it; #2,167 of 2,601 by trust here, #2,052 by citations
• Safety, Robustness & Interpretability — 20 citations into it
• Model Architectures — 13 citations into it
• Computational Mechanics — 10 citations into it; #7,640 of 10,001 by trust here, #6,041 by citations
• Scaling & Learning Theory — 9 citations into it; #276 of 428 by trust here, #347 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Michael Tao trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ian J. Goodfellow
5. Yann LeCun
6. Kaiming He
7. Karen Simonyan
8. Yoshua Bengio
9. L. Eon Bottou
10. Ilya Sutskever

Who trusts Michael Tao overall

1. Leslie N. Smith
2. Nicholay Topin
3. Charles H. Martin
4. Hao Li
5. Sai Praneeth Karimireddy
6. Daniel Jiwoong Im
7. Hesham Mostafa
8. Michael W. Mahoney

Papers from Michael Tao's perspective

Ordered by how strongly the researchers Michael Tao 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• An empirical analysis of the optimization of deep network loss surfaces (2016) — 66 citations
• Adversarial Geometry and Lighting using a Differentiable Renderer (2018) — 13 citations
• An Empirical Analysis of Deep Network Loss Surfaces (2016) — 48 citations