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Resonator networks for factoring distributed representations of data structures
[article]
2020
arXiv
pre-print
The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporting rule-based symbolic reasoning, a central property of cognition. Here we show how this may be accomplished within the framework of Vector Symbolic Architectures (VSA) (Plate, 1991; Gayler, 1998; Kanerva, 1996), whereby data structures are encoded by combining high-dimensional vectors with operations that together
arXiv:2007.03748v1
fatcat:itf74beq65a3znfqtakzdfpxpa