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The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software.
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Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D Sculley · 2017
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Learning curve prediction with Bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar
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Massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar
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Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, and Ion Stoica · 2018
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Automatic Machine Learning: Methods, Systems, Challenges
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PFDet: 2nd place solution to open images challenge 2018 object detection track
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