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Series in Communication Theory
Edited by Helmut Bölcskei
Vol. 16:
Michael Tobias
Tschannen
Unsupervised Learning:
Model-based clustering
and learned compression
1st Edition 2019. XVI, 208 pages. € 64,00.
ISBN
978-3-86628-637-5
Abstract:
This thesis
addresses two central tasks prevalent in many modern data processing, storage,
and transmission pipelines: Clustering and compression. Specifically, in the
first and the second part of this thesis, we study the problems of subspace
clustering and random process clustering, respectively. While clustering
problems are arguably among the most archetypal problems in unsupervised
learning, compression methods are traditionally hand designed. In the third and
fourth part of this thesis, we leverage machine learning techniques for compression,
a trend that only emerged recently. In more detail, we propose a deep generative
model-based framework for lossy data compression on
one hand, and we study compression of neural network models for inference on resource-constrained
hardware on the other hand.
About hte author:
Michael Tschannen
was born in 1988 in Lenzburg, Switzerland.
He obtained the B.Sc.
degree in 2012 from EPFL and the M.Sc. degree in 2014 from ETH Zürich, both in
Electrical Engineering. He graduated with the Dr. sc.
degree from ETH Zürich in 2018. In fall 2017 he was an Applied Scientist Intern
at Amazon AI in Palo Alto, CA. His research interests are in machine learning,
signal processing, and optimization.
Keywords: Subspace clustering; Random processes; Spectral clustering; Lossy compression; Model compression; Generative modeling;
Deep neural networks; Generative adversarial networks
Series in Communication Theory
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