Modern machine learning systems come with many design decisions (including hyperparameters, architectures of neural networks and the entire data processing pipeline), and the idea of automating these decisions gave rise to the research field of automated machine learning (AutoML). AutoML has been booming over the last decade, with hundreds of papers published each year now […]
Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
By Auto-PyTorch is a framework for automated deep learning (AutoDL) that uses BOHB as a backend to optimize the full deep learning pipeline, including data preprocessing, network training techniques and regularization methods. Auto-PyTorch is the successor of AutoNet which was one of the first frameworks to perform this joint optimization.
Best Practices for Scientific Research on Neural Architecture Search
By Neural architecture search (NAS) is currently one of the hottest topics in automated machine learning (see AutoML book), with a seemingly exponential increase in the number of papers written on the subject, see the figure above. While many NAS methods are fascinating (please see our survey article for an overview of the main trends […]