%0 Conference Proceedings %T Hyperparameter optimization of deep neural networks: combining Hperband with Bayesian model selection %+ Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES) %+ Département Images, Données, Signal (IDS) %+ MedisysResearch Lab (Medisys) %A Bertrand, Hadrien %A Ardon, Roberto %A Perrot, Matthieu %A Bloch, Isabelle %< avec comité de lecture %Z HB:CAP-17 %( CAp %B CAp %C Grenoble, France %8 2017 %D 2017 %K Deep Learning %K Gaussian Process %K Bayesian Optimization %K Hyperparameter Optimization %Z Computer Science [cs]/Machine Learning [cs.LG] %Z Computer Science [cs]/Medical Imaging %Z Computer Science [cs]/Image Processing [eess.IV]Conference papers %X One common problem in building deep learning architectures is the choice of the hyper-parameters. Among the various existing strategies, we propose to combine two complementary ones. On the one hand, the Hyperband method formalizes hyper-parameter optimization as a resource allocation problem, where the resource is the time to be distributed between many configurations to test. On the other hand, Bayesian optimization tries to model the hyper-parameter space as efficiently as possible to select the next model to train. Our approach is to model the space with a Gaussian process and sample the next group of models to evaluate with Hyperband. Preliminary results show a slight improvement over each method individually, suggesting the need and interest for further experiments. %G English %L hal-02412262 %U https://telecom-paris.hal.science/hal-02412262 %~ INSTITUT-TELECOM %~ ENST %~ TELECOM-PARISTECH %~ PARISTECH %~ UNIV-PARIS-SACLAY %~ TELECOM-PARISTECH-SACLAY %~ LTCI %~ IDS %~ IMAGES %~ INSTITUTS-TELECOM