Building on Deep Learning

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. Our view 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.
To be presented at the AAAI Workshop on Learning Rich Representations from Low-Level Sensors, Bellvue, WA, July 15, 2013. The original document contains color images.