Learning Shared Representations in a Multimodal Deep Boltzmann Machine
Keywords
No Thumbnail Available
Authors
Issue Date
2020-07-01
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
This Bachelor's thesis is about the ability of a multimodal Deep Boltzmann Ma-
chine (DBM) to form and use shared representations across two modalities. Such
shared representations are relevant for advanced robotics as well as for understand-
ing the human brain, for instance that of synesthetes. To investigate the formation
and usage of shared representations I implemented a multimodal DBM in Python
and let it perform image reconstruction tasks under di erent experimental condi-
tions. My results suggest that a multimodal DBM may not be well-suited to form
and use shared representations.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen