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Meta-learners' learning dynamics are unlike learners'
[article]
2019
arXiv
pre-print
Meta-learning is a tool that allows us to build sample-efficient learning systems. Here we show that, once meta-trained, LSTM Meta-Learners aren't just faster learners than their sample-inefficient deep learning (DL) and reinforcement learning (RL) brethren, but that they actually pursue fundamentally different learning trajectories. We study their learning dynamics on three sets of structured tasks for which the corresponding learning dynamics of DL and RL systems have been previously
arXiv:1905.01320v1
fatcat:okuureeiwrblhp7s3nltwojl3i