Alexandra Johnson

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 Alexandra Johnson trusts overall

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

1. James Bergstra
2. Jasper Snoek
3. Katharina Eggensperger

Papers from Alexandra Johnson's perspective

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

1. Practical Bayesian Optimization of Machine Learning Algorithms
2. Random Search for Hyper-Parameter Optimization
3. Algorithms for Hyper-Parameter Optimization
4. Sequential Model-Based Optimization for General Algorithm Configuration
5. ImageNet classification with deep convolutional neural networks
6. Scalable Bayesian Optimization Using Deep Neural Networks
7. Input Warping for Bayesian Optimization of Non-Stationary Functions
8. Learning Multiple Layers of Features from Tiny Images
9. Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters
10. Statistical Comparisons of Classifiers over Multiple Data Sets

Their papers

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