AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Building on Deep Learning
Marc Pickett

Last modified: 2013-06-29

Abstract


We propose using deep learning as the "workhorse" of a cognitive architecture. We show how deep learning can be leveraged to learn representations, such as a hierarchy of analogical schemas, from relational data. This approach to higher cognition drives some desiderata of deep learning, particularly modality independence and the ability to make top-down predictions. Finally, we consider the problem of how relational representations might be learned from sensor data that is not explicitly relational.

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