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{
    "pk": 28732,
    "title": "Learning deep taxonomic priors for concept learning from few positive examples",
    "subtitle": null,
    "abstract": "Human concept learning is surprisingly robust, allowing forprecise generalizations given only a few positive examples.Bayesian formulations that account for this behavior requireelaborate, pre-specified priors, leaving much of the learningprocess unexplained. More recent models of concept learningbootstrap from deep representations, but the deep neural net-works are themselves trained using millions of positive and neg-ative examples. In machine learning, recent progress in meta-learning has provided large-scale learning algorithms that canlearn new concepts from a few examples, but these approachesstill assume access to implicit negative evidence. In this paper,we formulate a training paradigm that allows a meta-learningalgorithm to solve the problem of concept learning from fewpositive examples. The algorithm discovers a taxonomic prioruseful for learning novel concepts even from held-out supercat-egories and mimics human generalization behavior—the firstto do so without hand-specified domain knowledge or negativeexamples of a novel concept.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "concept learning; deep neural networks; objecttaxonomies"
        }
    ],
    "section": "Papers with Poster Presentations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/8q32x650",
    "frozenauthors": [
        {
            "first_name": "Erin",
            "middle_name": "",
            "last_name": "Grant",
            "name_suffix": "",
            "institution": "University of California, Berkeley",
            "department": ""
        },
        {
            "first_name": "Joshua",
            "middle_name": "C.",
            "last_name": "Peterson",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        },
        {
            "first_name": "Thomas",
            "middle_name": "L.",
            "last_name": "Griffiths",
            "name_suffix": "",
            "institution": "Princeton University",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2019-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/28732/galley/18603/download/"
        }
    ]
}