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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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Kataoka, H., Okayasu, K., Matsumoto, A., Yamagata, E., Yamada, R., Inoue, N., Nakamura, A., & Satoh, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01555-8 · Cited by 59
Curriculum learning for human compositional generalization
Dekker, R., Otto, F., & Summerfield, C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205582119 · Cited by 63
“These phenomena are often attributed to resource rationality, whereby the best possible policy is pursued given limits on cognitive capacity, but our work suggests an alternative: local learning may be beneficial where it allows simple rules to be composed to allow extrapolation away from the training data (48).”
BackgroundWhy Are We Not Teaching Machine Learning at High School? A Proposal
Evangelista, I., Blesio, G. G., & Benatti, E. A. (2018) · World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) · doi:10.1109/weef-gedc.2018.8629750 · Cited by 51
Wasserstein Dependency Measure for Representation LearningPreprint
Ozair, S., Lynch, C., Bengio, Y., van den Oord, A., Levine, S., & Sermanet, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11780 · Cited by 64
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
Hollenstein, N., Renggli, C., Glaus, B., Barrett, M., Tröndle, M., Langer, N., & Zhang, C. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.659410 · Cited by 57
“Unlike most machine learning models, one of the most striking aspects of human learning is the ability to learn new words or concepts from limited numbers of examples (Lake, Salakhutdinov, & Tenenbaum, 2015).”
BackgroundIn-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories
Kazemi, A., Sharifi, M. M., Laguna, A. F., Muller, F., Rajaei, R., Olivo, R. R., Kämpfe, T., Niemier, M. T., & Hu, X. S. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9474025 · Cited by 49
Graphic complexity in writing systems
Miton, H., & Morin, O. (2021) · Cognition · doi:10.1016/j.cognition.2021.104771 · Cited by 50
“Motor-program based estimates of complexity (Lake, Salakhutdinov, & Tenenbaum, 2015) could offer computational options for such future research.”
BackgroundCollective Decision of One-vs-Rest Networks for Open-Set Recognition
Jang, J., & Kim, C. O. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3189996 · Cited by 49
“In this section, we conduct extensive experiments, including ablation studies, sensitivity analysis, and comparisons with state-of-the art OSR methods, on seven datasets: MNIST [32], EMNIST [33], Omniglot [34], CIFAR-10 [35], CIFAR-100 [35], ImageNet [36], and LSUN [37].”
MethodYiu, E., Kosoy, E., & Alison Gopnik, C. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231201401 · Cited by 77
“Similarly, a number of AI approaches have integrated causal inference and theory formation into learning mechanisms in an attempt to design more human-like systems (Goyal & Bengio, 2022; Lake et al., 2015; Pearl, 2000).”
BackgroundOne-Shot Face Recognition via Generative Learning
Ding, Z., Guo, Y., Zhang, L., & Fu, Y. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00011 · Cited by 49
On Modulating the Gradient for Meta-learning
Simon, C., Koniusz, P., Nock, R., & Harandi, M. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_33 · Cited by 49
Online Few-Shot Gesture Learning on a Neuromorphic Processor
Stewart, K. M., Orchard, G., Shrestha, S. B., & Neftci, E. O. (2020) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2020.3032058 · Cited by 48
“Here, we focus on the latter approach for the following reasons: Firstly, the ability to solve difficult recognition tasks using few samples is a key capability of the brain [17].”
FeFET Multi-Bit Content-Addressable Memories for In-Memory Nearest Neighbor Search
Kazemi, A., Sharifi, M. M., Laguna, A. F., Muller, F., Yin, X., Kämpfe, T., Niemier, M. T., & Hu, X. S. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3136576 · Cited by 51
“We consider the Omniglot [26] and mini-Imagenet [1] datasets in our evaluations.”
MethodUncertainty in Model-Agnostic Meta-Learning using Variational Inference
Nguyen, C. C., Do, T.-T., & Carneiro, G. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093536 · Cited by 49
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Rothfuss, J., Fortuin, V., Josifoski, M., & Krause, A. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000523824 · Cited by 48
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
Huynh, D., & Elhamifar, E. (2020) · neural information processing systems · Cited by 50
Discriminative k-shot learning using probabilistic modelsPreprint
Bauer, M., Rojas-Carulla, M., Świątkowski, J., Schölkopf, B., & Turner, R. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00326 · Cited by 48
Context-Associative Hierarchical Memory Model for Human Activity Recognition and Prediction
Wang, L., Zhao, X., Si, Y., Cao, L., & Liu, Y. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2617079 · Cited by 47
A common probabilistic framework for perceptual and statistical learning
Fiser, J., & Lengyel, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.007 · Cited by 44
Meta-Learning for Few-Shot Plant Disease Detection
Chen, L., Cui, X., & Li, W. (2021) · Foods · doi:10.3390/foods10102441 · Cited by 51
“A Meta-Dataset [30] is composed of 10 public datasets including ILSVRC-2012 (ImageNet) [31], Omniglot [32], FGVC-Aircraft (Air-craft) [33], CUB-200-2011 (Birds) [34], Describable Textures (DTD) [35], QuickDraw [36], FGVCx Fungi (Fungi)[37], VGG Flower (Flower) [38], Traffic Signs (Signs) [39], and MSCOCO [40].”
BackgroundCapsule Networks with Max-Min NormalizationPreprint
Zhen, Z., Kleinhans, A., Sandhu, G., Patel, I., & Unnikrishnan, K. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.09662 · Cited by 58
Concept Learners for Few-Shot LearningPreprint
Cao, K., Brbić, M., & Leskovec, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07375 · Cited by 52
One-shot learning of view-invariant object representations in newborn chicks
Wood, J. N., & Wood, S. M. W. (2020) · Cognition · doi:10.1016/j.cognition.2020.104192 · Cited by 47
Human vs. supervised machine learning: Who learns patterns faster?
Kühl, N., Goutier, M., Baier, L., Wolff, C., & Martin, D. (2022) · Cognitive Systems Research · doi:10.1016/j.cogsys.2022.09.002 · Cited by 66
Efficient few-shot machine learning for classification of EBSD patterns
Kaufmann, K., Lane, H., Liu, X., & Vecchio, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-87557-5 · Cited by 47
Pu, J., Peng, W., & Chen, Y. (2021) · Wave Motion · doi:10.1016/j.wavemoti.2021.102823 · Cited by 46
Compact Optimization Learning for AC Optimal Power Flow
Park, S., Chen, W., Mak, T. W. K., & Hentenryck, P. V. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3313438 · Cited by 46
“This contrasts with data instances in computer vision, as exemplified by the MNIST [36], CIFAR10 [37], and Omniglot [38] datasets.”
ResultDeep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Xu, H., Zhi, S., Sun, S., Patel, V. M., & Liu, L. (2025) · ACM Computing Surveys · doi:10.1145/3718362 · Cited by 57
“Specifically, [144] samples sub-networks by dropping individual neurons or entire feature maps, then selects the most suitable sub-networks to form an ensemble for target domain learning.”
Zero-shot Learning of Classifiers from Natural Language Quantification
Srivastava, S., Labutov, I., & Mitchell, T. M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1029 · Cited by 46
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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