DOI: 10.1126/science.aab3050. Human-level concept learning through probabilistic program induction

Human-level concept learning through probabilistic program induction

10.1126/science.aab3050

Crossref journal-article

American Association for the Advancement of Science (AAAS)

Science (221)

Abstract

Handwritten characters drawn by a modelNot only do children learn effortlessly, they do so quickly and with a remarkable ability to use what they have learned as the raw material for creating new stuff. Lakeet al.describe a computational model that learns in a similar fashion and does so better than current deep learning algorithms. The model classifies, parses, and recreates handwritten characters, and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce.Science, this issue p.1332

Bibliography

Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332â1338.

Authors 3

Brenden M. Lake (first)
Ruslan Salakhutdinov (additional)
Joshua B. Tenenbaum (additional)

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Dates

Type	When
Created	9 years, 8 months ago (Dec. 13, 2015, 1:05 a.m.)
Deposited	2 months, 3 weeks ago (May 31, 2025, 3:31 p.m.)
Indexed	9 hours, 42 minutes ago (Aug. 24, 2025, 6:54 p.m.)
Issued	9 years, 8 months ago (Dec. 11, 2015)
Published	9 years, 8 months ago (Dec. 11, 2015)
Published Print	9 years, 8 months ago (Dec. 11, 2015)

Funders 6

Office of Naval Research 10.13039/100000006
Region: Americas
gov (National government)
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Army Research Office 10.13039/100000183
Region: Americas
gov (National government)
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1. U.S. Army Research Office
2. United States Army Research Office
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Canadian Institute for Advanced Research 10.13039/100007631
Region: Americas
gov (Research institutes and centers)
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2. Institut Canadien de Recherches Avancées
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Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038
Region: Americas
gov (National government)
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2. NSERC
3. CRSNG
NSF Science and Technology Center
Awards1
1. CCF-1231216
Moore-Sloan Data Science Environment at NYU

BibTeX

@article{Lake_2015, title={Human-level concept learning through probabilistic program induction}, volume={350}, ISSN={1095-9203}, url={http://dx.doi.org/10.1126/science.aab3050}, DOI={10.1126/science.aab3050}, number={6266}, journal={Science}, publisher={American Association for the Advancement of Science (AAAS)}, author={Lake, Brenden M. and Salakhutdinov, Ruslan and Tenenbaum, Joshua B.}, year={2015}, month=dec, pages={1332–1338} }

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