Learning Shared Representations in a Multimodal Deep Boltzmann Machine
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2020-07-01
Language
en
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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.
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Faculteit der Sociale Wetenschappen