Encoding structure in holographic reduced representations.
By Matthew Alex Kelly, Dorothea Blostein, Douglas J. K. Mewhort. 2013
43 citations recorded by the source corpus.
Vector Symbolic Architectures (VSAs) such as Holographic Reduced Representations (HRRs) are computational associative memories used by cognitive psychologists to model behavioural and neurological aspects of human memory. We present a novel analysis of the mathematics of VSAs and a novel technique for representing data in HRRs. Encoding and decoding in VSAs can be characterised by Latin squares. Successful encoding requires the structure of the data to be orthogonal to the structure of the Latin squares. However, HRRs can successfully encode vectors of locally structured data if vectors are shuffled. Shuffling results are illustrated using images but are applicable to any nonrandom data. The ability to use locally structured vectors provides a technique for detailed modelling of stimuli in HRR models.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
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In Artificial Intelligence
- Sjoerd van Steenkiste
- J. Urgen Schmidhuber
- Klaus Greff
- Yoshua Bengio
- Sergey Levine
- Quoc V. Le
- Mohit Bansal
- Kyunghyun Cho
In Electrical and Electronic Engineering
Citations in the corpus
- On the Binding Problem in Artificial Neural Networks (2020, background): “TPRs were first introduced in Smolensky (1990) and several modifications have since been proposed that consider different binding, unbinding, and conjunction operations (Plate, 1995; Kanerva, 1996; Gayler, 1998; see Kelly et al., 2013 for an overview).”