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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
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053
The formation of learning sets.
Harlow, H. F. (1949) · Psychological Review · doi:10.1037/h0062474 · Cited by 1,959
The Architecture of Cognitive Control in the Human Prefrontal Cortex
Koechlin, É., Ody, C., & Kouneiher, F. (2003) · Science · doi:10.1126/science.1088545 · Cited by 1,939
Baldwin, J. M. (1896) · The American Naturalist · doi:10.1086/276408 · Cited by 1,781
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Meta-learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., & Lillicrap, T. (2016) · International Conference on Machine Learning · Cited by 1,504
Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv · doi:10.48550/arxiv.1606.04474 · Cited by 1,486
Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. C. (1992) · Psychological Review · doi:10.1037/0033-295x.99.4.605 · Cited by 1,410
Thrun, S., & Pratt, L. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5529-2 · Cited by 1,291
Rule Learning by Seven-Month-Old Infants
Marcus, G., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999) · Science · doi:10.1126/science.283.5398.77 · Cited by 1,246
Schemas and Memory Consolidation.
Tse, D., Langston, R. F., Kakeyama, M., Bethus, I., Spooner, P. A., Wood, E. R., Witter, M. P., & Morris, R. G. M. (2007) · Science · doi:10.1126/science.1135935 · Cited by 1,244
An information theoretical approach to prefrontal executive function
Koechlin, E., & Summerfield, C. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.04.005 · Cited by 1,183
O’Reilly, R. C., & Frank, M. J. (2005) · Neural Computation · doi:10.1162/089976606775093909 · Cited by 1,124
Spelke, E. S., & Kinzler, K. D. (2007) · Developmental Science · doi:10.1111/j.1467-7687.2007.00569.x · Cited by 1,089
What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior
Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.002 · Cited by 1,055
A deep learning framework for neuroscience
Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0520-2 · Cited by 1,027
Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes
Badre, D. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.02.004 · Cited by 980
A Simple Neural Attentive Meta-LearnerPreprint
Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.03141 · Cited by 961
Organizing conceptual knowledge in humans with a gridlike code
Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J. (2016) · Science · doi:10.1126/science.aaf0941 · Cited by 937
How schema and novelty augment memory formation
van Kesteren, M. T. R., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.02.001 · Cited by 860
Metalearning and neuromodulation
Doya, K. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00044-8 · Cited by 704
How Learning Can Guide Evolution.
Hinton, G. E., & Nowlan, S. J. (1987) · Cited by 682
Reinforcement learning, fast and slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.006 · Cited by 667
Self-citationNeural computations underlying arbitration between model-based and model-free learning
Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.028 · Cited by 642
A critique of pure learning and what artificial neural networks can learn from animal brains
Zador, A. M. (2019) · Nature Communications · doi:10.1038/s41467-019-11786-6 · Cited by 598
Prefrontal cortex as a meta-reinforcement learning system
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0147-8 · Cited by 596
Self-citationLearning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv · doi:10.48550/arxiv.1611.05763 · Cited by 579
Self-citationA solution to the learning dilemma for recurrent networks of spiking neurons
Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R. A., & Maass, W. (2020) · Nature Communications · doi:10.1038/s41467-020-17236-y · Cited by 568
Meta-Learning: A SurveyPreprint
Vanschoren, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03548 · Cited by 548
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.044 · Cited by 544
Learning to Learn: Introduction and Overview
Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521
Deep Neural Networks as Scientific Models
Cichy, R. M., & KAISER, D. H. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.01.009 · Cited by 496
Cognitive control over learning: Creating, clustering, and generalizing task-set structure.
Collins, A. G. E., & Frank, M. J. (2013) · Psychological Review · doi:10.1037/a0030852 · Cited by 426
Preschool children can learn to transfer: Learning to learn and learning from example
Brown, A. L., & Kane, M. J. (1988) · Cognitive Psychology · doi:10.1016/0010-0285(88)90014-x · Cited by 424
Prefrontal cortex and flexible cognitive control: Rules without symbols
Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502455102 · Cited by 414
Foundations of human reasoning in the prefrontal cortex
Donoso, M., Collins, A. G. E., & Koechlin, É. (2014) · Science · doi:10.1126/science.1252254 · Cited by 407
Learning a synaptic learning rule
Bengio, Y., Bengio, S., & Cloutier, J. (1991) · IJCNN-91-Seattle International Joint Conference on Neural Networks · doi:10.1109/ijcnn.1991.155621 · Cited by 402
Recasting Gradient-Based Meta-Learning as Hierarchical BayesPreprint
Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.08930 · Cited by 388
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks
Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.002 · Cited by 387
Botvinick, M., & Plaut, D. C. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.395 · Cited by 375
Frontal Cortex and the Discovery of Abstract Action Rules
Badre, D., Kayser, A. S., & D’Esposito, M. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.025 · Cited by 346
Learning latent structure: carving nature at its joints
Gershman, S. J., & Niv, Y. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.02.008 · Cited by 326
Long short-term memory and learning-to-learn in networks of spiking neuronsPreprint
Bellec, G., Salaj, D., Subramoney, A., Legenstein, R. A., & Maass, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09574 · Cited by 283
Deep reinforcement learning and its neuroscientific implications
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269
Self-citationMeta-learning in Reinforcement Learning
Schweighofer, N., & Doya, K. (2003) · Neural Networks · doi:10.1016/s0893-6080(02)00228-9 · Cited by 261
Meta-Gradient Reinforcement LearningPreprint
Xu, Z., van Hasselt, H. P., & Silver, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09801 · Cited by 227
Solway, A., Diuk, C., Córdova, N. I., Yee, D. M., Barto, A. G., Niv, Y., & Botvinick, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003779 · Cited by 154
Doing more with less: meta-reasoning and meta-learning in humans and machines
Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.01.005 · Cited by 139
Unsupervised Predictive Memory in a Goal-Directed AgentPreprint
Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska‐Barwińska, A., Rae, J. W., Mirowski, P., Leibo, J. Z., Santoro, A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10760 · Cited by 137
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119
Simple Principles of Metalearning
Schmidhuber, J., Zhao, J., & Wiering, M. (1996) · doi:10.71910/supsi.7463 · Cited by 69
Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters
Khamassi, M., Enel, P., Dominey, P. F., & Procyk, E. (2013) · Progress in brain research · doi:10.1016/b978-0-444-62604-2.00022-8 · Cited by 67
Been There, Done That: Meta-Learning with Episodic RecallPreprint
Ritter, S., Wang, J. X., Kurth-Nelson, Z. L., Jayakumar, S. M., Blundell, C., Pascanu, R., & Botvinick, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09692 · Cited by 60
Self-citation8-Month-Old Infants Spontaneously Learn and Generalize Hierarchical Rules
Werchan, D. M., Collins, A. G. E., Frank, M. J., & Amso, D. (2015) · Psychological Science · doi:10.1177/0956797615571442 · Cited by 58
Meta-learning of Sequential Strategies
Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv · doi:10.48550/arxiv.1905.03030 · Cited by 47
Self-citationA Self-Tuning Actor-Critic AlgorithmPreprint
Zahavy, T., Xu, Z., Veeriah, V., Hessel, M., Oh, J., van Hasselt, H. P., Silver, D., & Singh, S. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12928 · Cited by 46
Learning to represent reward structure: A key to adapting to complex environments
Nakahara, H., & Hikosaka, O. (2012) · Neuroscience Research · doi:10.1016/j.neures.2012.09.007 · Cited by 30
Episodic Control as Meta-Reinforcement LearningPreprint
Ritter, S., Wang, J. S., Kurth-Nelson, Z. L., & Botvinick, M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/360537 · Cited by 20
Self-citationMeta-learning by the Baldwin effect
Fernando, C., Sygnowski, J., Osindero, S., Wang, J., Schaul, T., Teplyashin, D., Sprechmann, P., Pritzel, A., & Rusu, A. A. (2018) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3205651.3208249 · Cited by 18
Self-citationDezfouli, A., Morris, R. W., Ramos, F., Dayan, P., & Balleine, B. W. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/328849 · Cited by 16
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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Gharoun, H., Momenifar, F., Chen, F., & Gandomi, A. H. (2024) · ACM Computing Surveys · doi:10.1145/3659943 · Cited by 249
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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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