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
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“In our study, we consider one-and few-shot learning in the context of the Omniglot dataset 34 —a commonly used benchmark for few-shot learning 12 —that contains 1,623 characters from 50 different alphabets.”
MethodOne-shot Learning with Memory-Augmented Neural NetworksPreprint
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06065 · Cited by 515
Improving Federated Learning Personalization via Model Agnostic Meta Learning
Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019) · arXiv · doi:10.48550/arxiv.1909.12488 · Cited by 381
“We recognize that some MAML datasets (Lake et al., 2015) do not admit a good notion of initial accuracy.”
BackgroundMeta-Learning to Detect Rare Objects
Wang, Y.-X., Ramanan, D., & Hebert, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01002 · Cited by 380
Machine learning: Accelerating materials development for energy storage and conversion
Chen, A., Zhang, X., & Zhou, Z. P. (2020) · InfoMat · doi:10.1002/inf2.12094 · Cited by 381
“The key is learning to learn, also called meta learning, which has been used in image recognition.(208,209) Several approaches(210,211) were developed for meta learning and it is worth introducing to materials science for limited datasets.”
MethodLearning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., & Finn, C. (2018) · arXiv · doi:10.48550/arxiv.1803.11347 · Cited by 370
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few ExamplesPreprint
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03096 · Cited by 428
A Broader Study of Cross-Domain Few-Shot Learning
Guo, Y., Codella, N., Karlinsky, L., Codella, J., Smith, J. R., Saenko, K., Rosing, T., & Feris, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_8 · Cited by 371
“Few-shot learning Few-shot learning [32,60,31] is an increasingly important topic in machine learning.”
BackgroundCausal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353
“Note that this assumption is not ad hoc in our model, in fact, it underpins almost every concept learning method from the classic Deformable Part Models [13] to modern CNNs [15], whose cognitive evidence can be found in [24].”
MethodGünther, F., Rinaldi, L., & Marelli, M. (2019) · Perspectives on Psychological Science · doi:10.1177/1745691619861372 · Cited by 354
Meta-Learning for Low-Resource Neural Machine Translation
Gu, J., Wang, Y., Chen, Y., Li, V. O. K., & Cho, K. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1398 · Cited by 359
Inverting the Generator of a Generative Adversarial Network
Creswell, A., & Bharath, A. A. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2875194 · Cited by 332
A Survey of Transfer Learning for Convolutional Neural Networks
Ribani, R., & Marengoni, M. (2019) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi-t.2019.00010 · Cited by 343
Counterfactual Visual ExplanationsPreprint
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.07451 · Cited by 341
Memory Matching Networks for One-Shot Image Recognition
Cai, Q., Pan, Y., Yao, T., Yan, C. C., & Mei, T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00429 · Cited by 320
Learning to Discover Novel Visual Categories via Deep Transfer Clustering
Han, K., Vedaldi, A., & Zisserman, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00849 · Cited by 320
Semantic memory: A review of methods, models, and current challenges
Kumar, A. A. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01792-x · Cited by 350
Cross-Domain Few-Shot Classification via Learned Feature-Wise TransformationPreprint
Tseng, H.-Y., Lee, H.-Y., Huang, J., & Yang, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.08735 · Cited by 368
“Few-shot classification (Lake et al., 2015) aims to recognize instances from novel categories (query instances) with only few labeled examples in each class (support examples).”
BackgroundLarge-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
Xu, Z., Zhou, T., Ma, M., Deng, C., Dai, Q., & Fang, L. (2024) · Science · doi:10.1126/science.adl1203 · Cited by 348
Shukla, K., Leoni, P. C. D., Blackshire, J. L., Sparkman, D. M., & Karniadakis, G. E. (2020) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-020-00705-1 · Cited by 304
Oh, C., Song, J., Choi, J., Kim, S., Lee, S., & Suh, B. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174223 · Cited by 301
One-Shot Relational Learning for Knowledge Graphs
Xiong, W., Yu, M., Chang, S., Guo, X., & Wang, W. Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1223 · Cited by 323
Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Wang, Y.-X., & Hebert, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_37 · Cited by 296
Compositional Visual Generation with Composable Diffusion Models
Liu, N., Li, S., Du, Y., Torralba, A. B., & Tenenbaum, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19790-1_26 · Cited by 365
Self-citation
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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