@inproceedings {runeMedico2018,
	title = {Automatic Hyperparameter Optimization in Keras for the MediaEval 2018 Medico Multimedia Task},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	year = {2018},
	publisher = {CEUR Workshop Proceedings (CEUR-WS.org)},
	abstract = {This paper details the approach to the MediaEval 2018 Medico Multimedia Task made by the Rune team. The decided upon approach uses a work-in-progress hyperparameter optimization system called Saga. Saga is a system for creating the best hyperparameter finding in Keras, a popular machine learning framework, using Bayesian optimization and transfer learning. In addition to optimizing the Keras classifier configuration, we try manipulating the dataset by adding extra images in a class lacking in images and splitting a commonly misclassified class into two classes.},
	keywords = {automatic hyperparameter optimization, Bayesian optimization, CNN, convolutional neural networks, dataset manipulation, gpyopt, hyperparameter optimization, keras, saga, tensorflow, Transfer Learning},
	author = {Borgli, Rune and Halvorsen, P{\r a}l and Riegler, Michael and H{\r a}kon Kvale {Stensland}}
}
