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{
    "pk": 31799,
    "title": "Representation of Variables and Their Values in Neural Networks",
    "subtitle": null,
    "abstract": "Neural nets (NNs) such as multi-layer\nfeedforward and recurrent nets have had\nconsiderable success in creating representations in\nthe hidden layers. In a combinatorial domain,\nsuch as a visual scene, a parsimonious represent-\nation might be in terms of component features (or\nvariables) such as colour, shape and size (each of\nwhich can take on multiple values, such as red or\ngreen, or square or circle). Simulations are\ndescribed demonstrating that a multi-variable\nencoder network can learn to represent an input\npattern in terms of its component variables,\nwherein each variable is encoded by a pair of\nhidden units. The interesting aspect of this\nrepresentation is that the number of hidden units\nrequired to represent arbitrary numbers of\nvariables and values is linear in the number of\nvariables, but constant with respect to the number\nof values for each variable. This result provides a\nnew perspective for assessing the representational\ncapacity of hidden units in combinatorial\ndomains.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Submitted Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/7jg5b5j3",
    "frozenauthors": [
        {
            "first_name": "Janet",
            "middle_name": "",
            "last_name": "Wiles",
            "name_suffix": "",
            "institution": "University of Queensland",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1993-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31799/galley/22867/download/"
        }
    ]
}