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Maintained by Difan Deng and Marius Lindauer. The following list considers papers related to neural architecture search. It is by no means complete. If you miss a paper on the list, please let us know. Please note that although NAS methods steadily improve, the quality of empirical evaluations in this field are still lagging behind compared to other areas in machine learning, AI and optimization.
AutoML: Methods, Systems, Challenges (first book on AutoML) Editors: Frank Hutter, Lars Kotthoff, Joaquin Vanschoren This is an open-access book; here is an entirely free complete PDF of the book, and a bibtex entry for it. Below, you can find the individual chapters and bibtex entries for them. If you would like to purchase a hard cover, please see Springer’s website for the book, or order the bo
AutoML … … provides methods and processes to make machine learning more accessible improve efficiency of machine learning systems accelerate research and AI application development Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform manual
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