hyperopt: Distributed Asynchronous Hyper-parameter Optimization. Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Ariafar, Setareh; Coll-Font, Jaume; Brooks, Dana; Dy, Jennifer: ADMMBO: Bayesian optimization with unknown constraints using ADMM (2019)
- Mariappan, Ragunathan; Rajan, Vaibhav: Deep collective matrix factorization for augmented multi-view learning (2019)
- Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
- Chan, Shing; Elsheikh, Ahmed H.: A machine learning approach for efficient uncertainty quantification using multiscale methods (2018)
- Kordík, Pavel; Černý, Jan; Frýda, Tomáš: Discovering predictive ensembles for transfer learning and meta-learning (2018)
- Li, Lisha; Jamieson, Kevin; DeSalvo, Giulia; Rostamizadeh, Afshin; Talwalkar, Ameet: Hyperband: a novel bandit-based approach to hyperparameter optimization (2018)
- Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, Michel Lang: mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions (2017) arXiv
- Mısır, Mustafa; Sebag, Michèle: \textscAlors: an algorithm recommender system (2017)
- Krueger, Tammo; Panknin, Danny; Braun, Mikio: Fast cross-validation via sequential testing (2015)
- Schulz, Hannes; Cho, Kyunghyun; Raiko, Tapani; Behnke, Sven: Two-layer contractive encodings for learning stable nonlinear features (2015)