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Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community.
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) · 1905
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Statistical learning by 8-month-old infants
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · 1928
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The formation of learning sets
Harlow, H. F. (1949) · 1949
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How learning can guide evolution
Hinton, G. E. & Nowlan, S. J. (1987) · 1987
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Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Schmidhuber, J. (1987) · 1987
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Preschool children can learn to transfer: Learning to learn and learning from example
Brown, A. L. & Kane, M. J. (1988) · 1988
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Learning a synaptic learning rule
Bengio, Y., Bengio, S., & Cloutier, J. (1991) · 1991
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Origins of knowledge
Spelke, E. S., Breinlinger, K., Macomber, J., & Jacobson, K. (1992) · 1992
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A neural network that embeds its own meta-levels
Schmidhuber, J. (1993) · 1993
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Metacognition: Knowing about knowing
Metcalfe, J., Shimamura, A. P., et al. (1994) · 1994
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Simple principles of metalearning
Schmidhuber, J., Zhao, J., & Wiering, M. (1996) · 1996
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Theoretical models of learning to learn
Baxter, J. (1998) · 1998
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Learning to learn: Introduction and overview
Thrun, S. & Pratt, L. (1998) · 1998
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The scientist in the crib: Minds, brains, and how children learn
Gopnik, A., Meltzoff, A. N., & Kuhl, P. K. (1999) · 1999
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Rule learning by seven-month-old infants
Marcus, G. F., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999) · 1999
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How people learn, vol. 11
Bransford, J. D., Brown, A. L., Cocking, R. R., et al. (2000) · 2000
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Metalearning and neuromodulation
Doya, K. (2002) · 2002
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Self-tuning deep reinforcement learning
Zahavy, T., Xu, Z., Veeriah, V., Hessel, M., Oh, J., van Hasselt, H., Silver, D., & Singh, S. (2020) · 2002
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The architecture of cognitive control in the human prefrontal cortex
Koechlin, E., Ody, C., & Kouneiher, F. (2003) · 2003
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Meta-learning in reinforcement learning
Schweighofer, N. & Doya, K. (2003) · 2003
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Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action
Botvinick, M. & Plaut, D. C. (2004) · 2004
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Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control
Daw, N. D., Niv, Y., & Dayan, P. (2005) · 2005
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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) · 2005
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Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia
O’Reilly, R. C. & Frank, M. J. (2006) · 2006
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Learning the value of information in an uncertain world
Behrens, T. E., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. (2007) · 2007
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An information theoretical approach to prefrontal executive function
Koechlin, E. & Summerfield, C. (2007) · 2007
Cited alongside, same era.
Core knowledge
Spelke, E. S. & Kinzler, K. D. (2007) · 2007
Cited alongside, same era.
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. (2007) · 2007
Cited alongside, same era.
Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes
Badre, D. (2008) · 2008
Cited alongside, same era.
Frontal cortex and the discovery of abstract action rules
Badre, D., Kayser, A. S., & D’Esposito, M. (2010) · 2010
Cited alongside, same era.
Learning latent structure: carving nature at its joints
Gershman, S. J. & Niv, Y. (2010) · 2010
Cited alongside, same era.
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. & DiCarlo, J. J. (2016) · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., & Levine, S. (2017) · 2017
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Neuroscience-inspired artificial intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · 2017
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Population based training of neural networks
Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al. (2017) · 2017
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · 2017
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A simple neural attentive meta-learner
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Learning to represent reward structure: A key to adapting to complex environments
Nakahara, H. & Hikosaka, O. (2012) · 2012
Cited alongside, same era.
How schema and novelty augment memory formation
Van Kesteren, M. T., Ruiter, D. J., Fernández, G., & Henson, R. N. (2012) · 2012
Cited alongside, same era.
Cognitive control over learning: Creating, clustering, and generalizing task-set structure
Collins, A. G. & Frank, M. J. (2013) · 2013
Cited alongside, same era.
Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters
Khamassi, M., Enel, P., Dominey, P. F., & Procyk, E. (2013) · 2013
Cited alongside, same era.
Foundations of human reasoning in the prefrontal cortex
Donoso, M., Collins, A. G., & Koechlin, E. (2014) · 2014
Cited alongside, same era.
Neural computations underlying arbitration between model-based and model-free learning
Lee, S. W., Shimojo, S., & O’Doherty, J. P. (2014) · 2014
Cited alongside, same era.
Mishra, N., Rohaninejad, M., Chen, X., & Abbeel, P. (2017) · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., & Zemel, R. (2017) · 2017
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What is a cognitive map? organizing knowledge for flexible behavior
Behrens, T. E., Muller, T. H., Whittington, J. C., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · 2018
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Long short-term memory and learning-to-learn in networks of spiking neurons
Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., & Maass, W. (2018) · 2018
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Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
Dezfouli, A., Morris, R., Ramos, F. T., Dayan, P., & Balleine, B. (2018) · 2018
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Meta-learning by the baldwin effect
Fernando, C., Sygnowski, J., Osindero, S., Wang, J., Schaul, T., Teplyashin, D., Sprechmann, P., Pritzel, A., & Rusu, A. (2018) · 2018
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Recasting gradient-based meta-learning as hierarchical bayes
Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. (2018) · 2018
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A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy
Kell, A. J., Yamins, D. L., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018) · 2018
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Vanschoren, J. (2018) · 2018
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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) · 2018
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Unsupervised predictive memory in a goal-directed agent
Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska-Barwinska, A., Rae, J., Mirowski, P., Leibo, J. Z., Santoro, A., et al. (2018) · 2018
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Meta-gradient reinforcement learning
Xu, Z., van Hasselt, H. P., & Silver, D. (2018) · 2018
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Reinforcement learning, fast and slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · 2019
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Deep neural networks as scientific models
Cichy, R. M. & Kaiser, D. (2019) · 2019
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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) · 2019
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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) · 2019
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A critique of pure learning and what artificial neural networks can learn from animal brains
Zador, A. M. (2019) · 2019
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A solution to the learning dilemma for recurrent networks of spiking neurons
Bellec, G., Scherr, F., Subramoney, A., Hajek, E., Salaj, D., Legenstein, R., & Maass, W. (2020) · 2020
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Deep reinforcement learning and its neuroscientific implications
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · 2020
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Direct fit to nature: An evolutionary perspective on biological and artificial neural networks
Hasson, U., Nastase, S. A., & Goldstein, A. (2020) · 2020
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