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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In the citing paper
“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
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
(2014) · Cited by 33,419
Self-citationHuman-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,420
Recognition-by-components: A theory of human image understanding.
Biederman, I. (1987) · Psychological Review · doi:10.1037/0033-295x.94.2.115 · Cited by 5,621
Flagged first, then by citations
Research works printed but not linked: 21
- E. M. Markman Categorization and Naming in Children (MIT Press Cambridge MA 1989).
- Krizhevsky A., Sutskever I., Hinton G. E., Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012).
- N. D. Goodman J. B. Tenenbaum T. Gerstenberg Concepts: New Directions E. Margolis S. Laurence Eds. (MIT Press Cambridge MA 2015).
- R. Salakhutdinov J. Tenenbaum A. Torralba in JMLR Workshop and Conference Proceedings vol. 27 Unsupervised and Transfer Learning Workshop I. Guyon G. Dror V. Lemaire G. Taylor D. Silver Eds. (Microtome Brookline MA 2012) pp. 195–206.
- Mansinghka V. K., Kulkarni T. D., Perov Y. N., Tenenbaum J. B., Adv. Neural Inf. Process. Syst. 26, 1520–1528 (2013).
- M.-P. Dubuisson A. K. Jain in Proceedings of the 12th IAPR International Conference on Pattern Recognition Vol. 1 Conference A: Computer Vision and Image Processing Jerusalem Israel 9 to 13 October 1994 (IEEE New York 1994) pp. 566–568.
- G. Koch R. S. Zemel R. Salakhutdinov paper presented at ICML Deep Learning Workshop Lille France 10 and 11 July 2015.
- Hinton G. E., Nair V., Adv. Neural Inf. Process. Syst. 19, 515–522 (2006).
- P. Liang M. I. Jordan D. Klein in Proceedings of the 27th International Conference on Machine Learning Haifa Israel 21 to 25 June 2010 (International Machine Learning Society Princeton NJ 2010) pp. 639–646.
- I. Hwang A. Stuhlm¨ller N. D. Goodman (2011) http://arxiv.org/abs/1110.5667. · arXiv
- E. Dechter J. Malmaud R. P. Adams J. B. Tenenbaum in Proceedings of the 23rd International Joint Conference on Artificial Intelligence F. Rossi Ed. Beijing China 3 to 9 August 2013 (AAAI Press/International Joint Conferences on Artificial Intelligence Menlo Park CA 2013) pp. 1302–1309.
- J. Rule E. Dechter J. B. Tenenbaum in Proceedings of the 37th Annual Conference of the Cognitive Science Society D. C. Noelle et al . Eds. Pasadena CA 22 to 25 July 2015 (Cognitive Science Society Austin TX 2015) pp. 2051–2056.
- T. D. Ullman A. Stuhlmuller N. Goodman J. B. Tenenbaum in Proceedings of the 36th Annual Conference of the Cognitive Science Society Quebec City Canada 23 to 26 July 2014 (Cognitive Science Society Austin TX 2014) pp. 1640–1645.
- B. M. Lake C.-y. Lee J. R. Glass J. B. Tenenbaum in Proceedings of the 36th Annual Conference of the Cognitive Science Society Quebec City Canada 23 to 26 July 2014 (Cognitive Science Society Austin TX 2014) pp. 803–808.
- A. Graves (2014) http://arxiv.org/abs/1308.0850. · arXiv
- K. Gregor I. Danihelka A. Graves D. J. Rezende D. Wierstra in Proceedings of the International Conference on Machine Learning (ICML) Lille France 6 to 11 July 2015 (International Machine Learning Society Princeton NJ 2015) pp. 1462–1471.
- Chung J., et al.., Adv. Neural Inf. Process. Syst. 28, (2015).
- B. M. Lake R. Salakhutdinov J. B. Tenenbaum paper presented at the 34th Annual Conference of the Cognitive Science Society Sapporo Japan 1 to 4 August 2012.
- B. M. Lake R. Salakhutdinov J. B. Tenenbaum paper presented at the Advances in Neural Information Processing Systems 26 City Country Dates Month Year.
- B. M. Lake thesis MIT Cambridge MA (2014).
- T. J. O’Donnell thesis Harvard University Cambridge MA (2011).
Cited by
Sorted by citations
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Neural Architecture Search with Reinforcement LearningPreprint
Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 5,705
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
Generalizing from a Few Examples: A Survey on Few-shot Learning
Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2021) · ACM Computing Surveys · doi:10.1145/3386252 · Cited by 3,165
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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