Patrick Hayes

Artificial Intelligence · 2016

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Patrick Hayes trusts overall

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

1. James Bergstra
2. Jasper Snoek
3. Katharina Eggensperger
4. Frank Hutter
5. Ruben Martinez-Cantin

Papers from Patrick Hayes's perspective

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

1. Practical Bayesian Optimization of Machine Learning Algorithms
2. ImageNet classification with deep convolutional neural networks
3. Random Search for Hyper-Parameter Optimization
4. Algorithms for Hyper-Parameter Optimization
5. Learning Multiple Layers of Features from Tiny Images
6. Gradient-based learning applied to document recognition
7. Sequential Model-Based Optimization for General Algorithm Configuration
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Scalable Bayesian Optimization Using Deep Neural Networks

Their papers

• A Stratified Analysis of Bayesian Optimization Methods (2016) — 39 citations