Continuous Learning in a Hierarchical Multiscale Neural Network

Thomas Wolf; Julien Chaumond; Clement Delangue

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arXiv · 1805.05758

Continuous Learning in a Hierarchical Multiscale Neural Network

Thomas Wolf·Julien Chaumond·Clement Delangue

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Abstract

We reformulate the problem of encoding a multi-scale representation of a sequence in a language model by casting it in a continuous learning framework. We propose a hierarchical multi-scale language model in which short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time-scale dependencies are encoded in the dynamic of the lower-level network by having a meta-learner update the weights of the lower-level neural network in an online meta-learning fashion. We use elastic weights consolidation as a higher-level to prevent catastrophic forgetting in our continuous learning framework.

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Thomas Wolf, Julien Chaumond, Clement Delangue. 2018-05-15. Continuous Learning in a Hierarchical Multiscale Neural Network. https://arxiv.org/abs/1805.05758

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