{"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/"}]}