Distributed Evolution Strategies Using TPUs for Meta-Learning. (arXiv:2201.00093v1 [cs.NE])

Meta-learning traditionally relies on backpropagation through entire tasks to
iteratively improve a model's learning dynamics. However, this approach is
computationally intractable when scaled to complex tasks. We propose a
distributed evolutionary meta-learning strategy using Tensor Processing Units
(TPUs) that is highly parallel and scalable to arbitrarily long tasks with no
increase in memory cost. Using a Prototypical Network trained with evolution
strategies on the Omniglot dataset, we achieved an accuracy of 98.4% on a
5-shot classification problem. Our algorithm used …

arxiv distributed evolution learning meta strategies tpus