The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Human-level concept learning through probabilistic program induction
Abstract
Handwritten characters drawn by a model: Not 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. Lake et 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
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“Object detection algorithms are unable, in general, to recognize object categories outside of their training dataset, although ideally there should be the ability to recognize novel object categories (Lake et al. 2015;Hariharan and Girshick 2017).”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Lake et al. 2015
In the citing paper
“In contrast, humans (even children) can learn a visual concept quickly from very few given examples and can often generalize well (Biederman 1987b;Lake et al. 2015;FeiFei et al. 2006).”
From Deep Learning for Generic Object Detection: A Survey (2020) · cites this paper as Lake et al. 2015
In the citing paper
“Finally, higher-level systems in the brain may be able to implement Bayesian learning of sequential programs, which is a powerful means of one-shot learning (Lake et al., 2015).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Lake et al., 2015
In the citing paper
“In particular, sequential programs were found to enable one-shot learning of new geometric/visual concepts (Lake et al., 2015).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Lake et al., 2015
In the citing paper
“Structured, serial, hierarchical probabilistic programs have recently been shown to model aspects of human conceptual representation and compositional learning (Lake et al., 2015).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Lake et al., 2015
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Metadata
- Authors
- Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B.
- Affiliations
- New York University (US)
- University of Toronto (CA)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Lake, Brenden M.
- Venue
- Science
- Published
- 2015-12-11
- Volume
- 350
- Issue
- 6266
- Pages
- 1332-1338
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2194321275
- Cited by
- 2,817
- References
- 62
- Citations by year
- 2026: 137 · 2025: 186 · 2024: 256 · 2023: 281 · 2022: 300 · 2021: 415 · 2020: 400 · 2019: 393 · 2018: 223 · 2017: 145 · 2016: 79 · 2015: 1
- FWCI
- 146.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Office of Naval Research (W911NF-08-1-0242, W911NF-13-1-2012, N000141310333)
- Army Research Office
- Canadian Institute for Advanced Research
- Natural Sciences and Engineering Research Council of Canada
- NSF Science and Technology Center (CCF-1231216)
- Moore-Sloan Data Science Environment at NYU
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- concept learning, one-shot classification, handwritten character recognition, alphabet, visual concepts, visual Turing test
- MeSH terms
- Machine Learning, Algorithms, Bayes Theorem, Computer Simulation, Concept Formation, Generalization, Psychological, Humans
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