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{ "pk": 25705, "title": "Generating Hyperdimensional Distributed Representations from Continuous-\nValued Multivariate Sensory Input", "subtitle": null, "abstract": "Hyperdimensional computing (HDC) refers to the\nrepresentation and manipulation of data in a very high\ndimensional space using random vectors. Due to the high\ndimensionality, vectors of the space can code large amounts\nof information in a distributed manner, are robust to variation,\nand are easily distinguished from random noise. More\nimportantly, HDC can be used to represent compositional and\nhierarchical relationships and recursive operations between\nentities using fixed-size representations, making it intriguing\nfrom a cognitive modeling point of view. However, the\nmajority of the existing work in this area has focused on\nmodeling discrete categorical data. This paper presents a new\nmethod for mapping continuous-valued multivariate data into\nhypervectors, enabling construction of compositional\nrepresentations from non-categorical data. The mapping is\nstudied in a word classification task, showing how rich\ndistributed representations of spoken words can be encoded\nusing HDC-based representations", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "hyperdimensional computing; distributed\nrepresentations; speech recognition; memory" } ], "section": "Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/3db1b0x6", "frozenauthors": [ { "first_name": "Okko", "middle_name": "", "last_name": "Rasanen", "name_suffix": "", "institution": "Aalto University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2015-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/25705/galley/15329/download/" } ] }