Advancing the Theory and Utility of Holographic Reduced Representations
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In this thesis, we build upon the work of Plate by advancing the theory and utility of Holographic Reduced Representations (HRRs). HRRs are a type of linear, associative memory developed by Plate and are an implementation of Hinton’s reduced representations. HRRs and HRR-like representations have been used to model human memory, to model understanding analogies, and to model the semantics of natural language. However, in previous research, HRRs are restricted to storing and retrieving vectors of random numbers, limiting both the ability of HRRs to model human performance in detail, and the potential applications of HRRs. We delve into the theory of HRRs and develop techniques to store and retrieve images, or other kinds of structured data, in an HRR. We also investigate square matrix representations as an alternative to HRRs, and use iterative training algorithms to improve HRR performance. This work provides a foundation for cognitive modellers and computer scientists to explore new applications of HRRs.