Henry W. Lin

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 12 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #15,856 of 35,345 by trust here, #13,962 by citations
• Model Architectures — 6 citations into it
• Scaling & Learning Theory — 6 citations into it; #227 of 428 by trust here, #261 by citations

Whom Henry W. Lin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Max Tegmark
4. Yann LeCun
5. J. Håstad
6. George V. Cybenko
7. Matus Telgarsky
8. Guifré Vidal
9. Mehran Kardar
10. Alex Krizhevsky

Who trusts Henry W. Lin overall

1. Eleonora Cagli
2. Boris Hanin
3. Ziming Liu
4. Le Chang
5. Enrico Camporeale
6. Karl J. Friston
7. Cécile Canovas
8. Nikolaus Kriegeskorte
9. Nathan Oken Hodas
10. Shuo-Hui Li

Papers from Henry W. Lin's perspective

Ordered by how strongly the researchers Henry W. Lin 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Why Does Deep and Cheap Learning Work So Well? (2016) — 645 citations
• Criticality in Formal Languages and Statistical Physics∗ (2017)