Add to ORKGThis thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly black-box functions. Besides theoretical treatment of the topic, the focus of the thesis is on two numerical experiments. Firstly, different types of acquisition functions, which are the key components responsible for the performance, are tested and compared. Special emphasis is on the analysis of a so-called exploration-exploitation trade-off. Secondly, one of the most recent applications of Bayesian optimization concerns hyperparameter tuning in machine learning algorithms, where the objective function is expensive to evaluate and not given analytically. However, some results indicate that much simpler methods can give similar results. Our cont...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
This thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly blac...
This thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly blac...
Tuning hyperparameters of machine learning models is important for their performance. Bayesian optim...
This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. ...
Advances in machine learning have had, and continue to have, a profound effect on scientific researc...
Advances in machine learning have had, and continue to have, a profound effect on scientific researc...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural...
Progress in practical Bayesian optimization is hampered by the fact that the only available standard...
Bayesian optimization forms a set of powerful tools that allows efficient blackbox optimization and...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
This thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly blac...
This thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly blac...
Tuning hyperparameters of machine learning models is important for their performance. Bayesian optim...
This thesis addresses many open challenges in hyperparameter tuning of machine learning algorithms. ...
Advances in machine learning have had, and continue to have, a profound effect on scientific researc...
Advances in machine learning have had, and continue to have, a profound effect on scientific researc...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
The goal of this thesis was to implement a practical tool for optimizing hy- perparameters of neural...
Progress in practical Bayesian optimization is hampered by the fact that the only available standard...
Bayesian optimization forms a set of powerful tools that allows efficient blackbox optimization and...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Bayesian optimization has recently been proposed as a framework for automati-cally tuning the hyperp...
Deep neural networks have recently become astonishingly successful at many machine learning problems...
Deep neural networks have recently become astonishingly successful at many machine learning problems...