Tony Butler-Yeoman

Artificial Intelligence · 2016

A researcher in the Science corpus: 1 paper; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it
• Optimization & Training — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Model Architectures — 8 citations into it

Whom Tony Butler-Yeoman trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. N. Schraudolph
5. Martin A. Zinkevich
6. Ilya Sutskever
7. Sébastien Bubeck
8. Yann LeCun
9. Matus Telgarsky
10. John C. Duchi

Papers from Tony Butler-Yeoman's perspective

Ordered by how strongly the researchers Tony Butler-Yeoman 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. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks (2016) — 31 citations