Relational Learning with Variational Bayes

Kuang-Hung Liu

Keywords: Relational learning, unsupervised learning, variational inference, probabilistic graphical model

Abstract:

In psychology, relational learning refers to the ability to recognize and respond to
relationship among objects irrespective of the nature of those objects. Relational
learning has long been recognized as a hallmark of human cognition and a key
question in artificial intelligence research. In this work, we propose an unsupervised
learning method for addressing the relational learning problem where we
learn the underlying relationship between a pair of data irrespective of the nature
of those data. The central idea of the proposed method is to encapsulate the relational
learning problem with a probabilistic graphical model in which we perform
inference to learn about data relationships and other relational processing tasks.

One-sentence Summary: We propose an unsupervised learning method for addressing the relational learning problem where we learn the underlying relationship between a pair of data irrespective of the nature of those data.

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