The DOI matches its Crossref record; 71 of the 73 references it cites were found in a registry or an index.
Meta-learning in natural and artificial intelligence
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“By now, examples of this phenomenon have been found in many different species - including humans - across nature (Wang, 2021).”
From Meta-learned models of cognition (2024) · cites this paper as Wang, 2021
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Human-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
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512
Prototypical Networks for Few-shot LearningPreprint
Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 6,245
Statistical learning by 8-month-old infants
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · Science · doi:10.1126/science.274.5294.1926 · Cited by 5,659
Matching Networks for One Shot LearningPreprint
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 4,975
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817
Optimization as a Model for Few-Shot Learning
Ravi, S., & Larochelle, H. (2017) · International Conference on Learning Representations · Cited by 2,766
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
Research works printed but not linked: 1
- Ravi, 2016, Optimization as a model for fewshot learning, International Conference on Learning Representations
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Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey
Acharya, D. B., Kuppan, K., & Divya, B. (2025) · IEEE Access · doi:10.1109/access.2025.3532853 · Cited by 717
Meta-learning Approaches for Few-Shot Learning: A Survey of Recent Advances
Gharoun, H., Momenifar, F., Chen, F., & Gandomi, A. H. (2024) · ACM Computing Surveys · doi:10.1145/3659943 · Cited by 249
A substrate-less nanomesh receptor with meta-learning for rapid hand task recognition
Kim, K. K., Kim, M., Pyun, K. R., Kim, J., Min, J., Koh, S., Root, S. E., Kim, J., Nguyen, B. T., Nishio, Y., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00888-7 · Cited by 185
Abstraction and analogy‐making in artificial intelligence
Mitchell, M. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14619 · Cited by 125
Frenkel, C., Bol, D., & Indiveri, G. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3273520 · Cited by 160
Metadata
- Authors
- Wang, J. X.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Corresponding author
- Wang, Jane X
- ORCID
- Wang, Jane X
- Venue
- Current Opinion in Behavioral Sciences
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 38
- Pages
- 90-95
- ISSN
- 2352-1546 (ISSN-L)
- OpenAlex
- W3109949687
- Cited by
- 171
- References
- 73
- Citations by year
- 2026: 24 · 2025: 37 · 2024: 48 · 2023: 30 · 2022: 20 · 2021: 11
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Current Opinion in Behavioral Sciences (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- DeepMind
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- meta-learning, biological intelligence, artificial intelligence, cognitive science
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