Building on Deep Learning
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. 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2013
- Accession Number
- ADA580434
Entities
People
- Marc Pickett
Organizations
- United States Naval Research Laboratory