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Data-driven impulse response regularization via deep learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0002-4634-7240
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2018 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
2018. p. 1-6
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-366186DOI: 10.1016/j.ifacol.2018.09.081ISI: 000446599200002OAI: oai:DiVA.org:uu-366186DiVA, id: diva2:1265279
Conference
SYSID 2018, July 9–11, Stockholm, Sweden
Available from: 2018-10-08 Created: 2018-11-22 Last updated: 2022-04-04Bibliographically approved
In thesis
1. Deep learning applied to system identification: A probabilistic approach
Open this publication in new window or tab >>Deep learning applied to system identification: A probabilistic approach
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification.

Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes.

Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.

Place, publisher, year, edition, pages
Uppsala University, 2019
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2019-007
National Category
Signal Processing Probability Theory and Statistics
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-397563 (URN)
Supervisors
Available from: 2019-11-18 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved
2. Deep probabilistic models for sequential and hierarchical data
Open this publication in new window or tab >>Deep probabilistic models for sequential and hierarchical data
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Consider the problem where we want a computer program capable of recognizing a pedestrian on the road. This could be employed in a car to automatically apply the brakes to avoid an accident. Writing such a program is immensely difficult but what if we could instead use examples and let the program learn what characterizes a pedestrian from the examples. Machine learning can be described as the process of teaching a model (computer program) to predict something (the presence of a pedestrian) with help of data (examples) instead of through explicit programming.

This thesis focuses on a specific method in machine learning, called deep learning. This method can arguably be seen as sole responsible for the recent upswing of machine learning in academia as well as in society at large. However, deep learning requires, in human standards, a huge amount of data to perform well which can be a limiting factor.  In this thesis we describe different approaches to reduce the amount of data that is needed by encoding some of our prior knowledge about the problem into the model. To this end we focus on sequential and hierarchical data, such as speech and written language.

Representing sequential output is in general difficult due to the complexity of the output space. Here, we make use of a probabilistic approach focusing on sequential models in combination with a deep learning structure called the variational autoencoder. This is applied to a range of different problem settings, from system identification to speech modeling.

The results come in three parts. The first contribution focus on applications of deep learning to typical system identification problems, the intersection between the two areas and how they can benefit from each other. The second contribution is on hierarchical data where we promote a multiscale variational autoencoder inspired by image modeling. The final contribution is on verification of probabilistic models, in particular how to evaluate the validity of a probabilistic output, also known as calibration.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2022. p. 87
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2139
Keywords
Machine learning, Deep learning, Sequential modelling
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-470433 (URN)978-91-513-1478-5 (ISBN)
Public defence
2022-05-24, Sonja Lyttkens, 101121, Ångström, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2022-05-02 Created: 2022-04-04 Last updated: 2022-06-14

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Andersson, CarlWahlström, NiklasSchön, Thomas B.

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