Towards event analysis in time-series data: Asynchronous probabilistic models and learning from partial labels

Thesis type
(Thesis) Ph.D.
Date created
2021-03-10
Authors/Contributors
Abstract
In this thesis, we contribute in two main directions: modeling asynchronous time-series data and learning from partial labelled data. We first propose novel probabilistic frameworks to improve flexibility and expressiveness of current approaches in modeling complex real-world asynchronous event sequence data. Second, we present a scalable approach to end-to-end learn a deep multi-label classifier with partial labels. To evaluate the effectiveness of our proposed frameworks, we focus on visual recognition application, however, our proposed frameworks are generic and can be used in modeling general settings of learning event sequences, and learning multi-label classifiers from partial labels. Visual recognition is a fundamental piece for achieving machine intelligence, and has a wide range of applications such as human activity analysis, autonomous driving, surveillance and security, health-care monitoring, etc. With a wide range of experiments, we show that our proposed approaches help to build more powerful and effective visual recognition frameworks.
Document
Identifier
etd21284
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Mori, Greg
Language
English
Member of collection
Attachment Size
input_data\21371\etd21284.pdf 4.87 MB