Meta-Learning in Computational Intelligence

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One-shot Learning with Memory-Augmented Neural Networks (2016, background): “This kind of ﬂexible adaptation is a celebrated aspect of human learning (Jankowski et al., 2011), manifesting in settings ranging from motor control (Braun et al., 2009) to the acquisition of abstract concepts (Lake et al., 2015).”
Machine learning for molecular and materials science (2018, background): “Another promising solution to the problem of limited datasets is meta-learning, where knowledge is learned within and across problems.(85) New developments such as neural Turing machines(86) or imitation learning(87) are enabling the realisation of this process.”