New hybrid kernel architectures for deep learning

Mora de Checa, Daniel

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Mora de Checa, Daniel

Tutor / directorBelanche Muñoz, Luis Antonio

Document typeMaster thesis

Date2018-04

Rights accessOpen Access

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder

Abstract

In this work we explore the possibilities of combining neural network architectures and kernel methods by introducing hybrid kernel blocks. We present hybrid architectures which can be trained as traditional neural networks and introduce novel training and regularization methodologies for them.

SubjectsNeural networks (Computer science), Machine learning, Kernel functions, Xarxes neuronals (Informàtica), Aprenentatge automàtic, Kernel, Funcions de

DegreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2012)

URIhttp://hdl.handle.net/2117/119034

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Màsters oficials - Master in Artificial Intelligence - MAI [240]

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New hybrid kernel architectures for deep learning

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