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Learning deep autoregressive models for hierarchical data
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, Artificial Intelligence.ORCID iD: 0000-0002-1539-6314
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, Artificial Intelligence.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, Artificial Intelligence.ORCID iD: 0000-0001-5183-234X
2021 (English)In: IFAC PapersOnLine, Elsevier BV Elsevier, 2021, Vol. 54, no 7, p. 529-534Conference paper, Published paper (Refereed)
Abstract [en]

We propose a model for hierarchical structured data as an extension to the stochastic temporal convolutional network. The proposed model combines an autoregressive model with a hierarchical variational autoencoder and downsampling to achieve superior computational complexity. We evaluate the proposed model on two different types of sequential data: speech and handwritten text. The results are promising with the proposed model achieving state-of-the-art performance.

Place, publisher, year, edition, pages
Elsevier BV Elsevier, 2021. Vol. 54, no 7, p. 529-534
Keywords [en]
Deep learning, variational autoencoders, nonlinear systems
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-457738DOI: 10.1016/j.ifacol.2021.08.414ISI: 000696396200091OAI: oai:DiVA.org:uu-457738DiVA, id: diva2:1611007
Conference
19th IFAC Symposium on System Identification (SYSID), JUL 13-16, 2021, Padova, ITALY
Funder
Swedish Research CouncilKjell and Marta Beijer FoundationAvailable from: 2021-11-12 Created: 2021-11-12 Last updated: 2024-01-15Bibliographically approved
In thesis
1. 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, Carl R.Wahlström, NiklasSchön, Thomas B.

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