Article Instance

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{
    "pk": 31714,
    "title": "Rapid Unsupervised Learning of Object Structural Descriptions",
    "subtitle": null,
    "abstract": "A single view of an unfamiliar object\ntypically provides enough information about the\nobject's shape to permit recognition from a wide\nrange of n e w viewpoints. A recent model by\nH u m m e l and Biederman (1990, 1992) provides a\npartial account of this ability in terms of the\nactivation of viewpoint invariant structural\ndescriptions of (even unfamiliar) objects. W e\ndescribe the Structural Description Encoder (SDE),\na self-organizing feed-forward neural network that\nlearns such descriptions in one or at most two\nexposures. Rapid, reliable learning results from the\ninteractions among recruited and unrecruited units,\nwhose response characteristics are differentiated\nthrough the use of dynamic thresholds and learning\nrates.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "Submitted Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/2dk089n0",
    "frozenauthors": [
        {
            "first_name": "John",
            "middle_name": "E.",
            "last_name": "Hummel",
            "name_suffix": "",
            "institution": "University of California, Los Angeles",
            "department": ""
        },
        {
            "first_name": "Jun",
            "middle_name": "",
            "last_name": "Saiki",
            "name_suffix": "",
            "institution": "University of California, Los Angeles",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1993-01-01T10:00:00-08:00",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/31714/galley/22782/download/"
        }
    ]
}