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Catecholaminergic modulation of meta-learning [PDF]
eLife, 2019 The remarkable expedience of human learning is thought to be underpinned by meta-learning, whereby slow accumulative learning processes are rapidly adjusted to the current learning environment. To date, the neurobiological implementation of meta-learning
Jennifer L Cook +6 more
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A survey of deep meta-learning [PDF]
Artificial Intelligence Review, 2021 AbstractDeep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new conceptsquicklyis limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field ofDeep Meta-Learningadvances at great speed, but
Mike Huisman +2 more
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Meta-learning by the Baldwin effect [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018 The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan. To this date there has been no demonstration of its necessity in empirically challenging tasks. Here we show that the Baldwin effect is capable of evolving few-shot supervised and reinforcement learning mechanisms,
Chrisantha Fernando +8 more
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CoRR, 2022 In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from routing problems to online advertising auctions -- evolve dynamically, thereby leading to many similar games to be solved.
Keegan Harris +5 more
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Meta-Learning to Compositionally Generalize [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 ACL2021 Camera Ready; fix a small ...
Conklin, H. +3 more
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CoRR, 2020 In this paper, we introduce a discrete variant of the meta-learning framework. Meta-learning aims at exploiting prior experience and data to improve performance on future tasks. By now, there exist numerous formulations for meta-learning in the continuous domain.
Arman Adibi +2 more
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Meta-learned models of cognition
Behavioral and Brain Sciences, 2023 Abstract Psychologists and neuroscientists extensively rely on computational models for studying and analyzing the human mind. Traditionally, such computational models have been hand-designed by expert researchers. Two prominent examples are cognitive architectures and Bayesian models of cognition. Although the former requires the specification of a
Marcel Binz +5 more
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