A Comparative Study of Black-box Optimization Algorithms for Tuning of Hyper-parameters in Deep Neural Networks
Deep neural networks (DNNs) have successfully been applied across various data intensive applications ranging from computer vision, language modeling, bioinformatics and search engines. Hyper-parameters of a DNN are defined as parameters that remain fixed during model training and heavily influence...
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| Format: | Others |
| Language: | English |
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Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik
2018
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69865 |