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Series in Communication Theory
Edited by Helmut Bölcskei
Vol. 15:
Thomas Wiatowski
Harmonic Analysis of
Deep Convolutional
Neural Networks
1st Edition 2018. 214 pages. € 64,00.
ISBN
978-3-86628-610-8
A central task
in machine learning, computer vision, and signal processing is to extract
characteristic features of signals. Feature extractors based on deep
convolutional neural networks have been applied with significant success in a
wide range of practical machine learning tasks such as classification of images
in the ImageNet data set, image captioning, or control-policy-learning to play
Atari games or the board game Go. Since deep convolutional neural networks lead
to remarkable results across a broad range of applications, it is essential to
understand their underlying mechanisms. In this thesis, we develop a
mathematical theory of deep convolutional neural networks for feature
extraction using concepts from applied harmonic analysis. We investigate the
impact of network topology and building blocks - convolution filters, non-linearities, and pooling operators - on the network‘s
feature extraction capabilities.
About the author:
Thomas Wiatowski was born in Strzelce Opolskie, Poland, on December 20, 1987, and received the
BSc in Mathematics and the MSc in Mathematics from Technical University of
Munich, Germany, in 2010 and 2012, respectively. In 2012 he was a researcher at
the Institute of Computational Biology at Helmholtz Zentrum
in Munich, Germany. He joined ETH Zurich in 2013, where he graduated with the
Dr. sc. degree in 2017. His research interests are in
deep machine learning, mathematical signal processing, and applied harmonic
analysis.
Keywords: Frame theory; Machine learning, Convolutional neural networks, Feature
extraction, Deep learning
Series
in Communication Theory
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