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Minimum Description Length Recurrent Neural Networks

2021-10-31 21:43:31
Nur Lan, Michal Geyer, Emmanuel Chemla, Roni Katzir

Abstract

We train neural networks to optimize a Minimum Description Length score, i.e., to balance between the complexity of the network and its accuracy at a task. We show that networks trained with this objective function master tasks involving memory challenges such as counting, including cases that go beyond context-free languages. These learners master grammars for, e.g., $a^nb^n$, $a^nb^nc^n$, $a^nb^{2n}$, and $a^nb^mc^{n+m}$, and they perform addition. They do so with 100% accuracy, sometimes also with 100% confidence. The networks are also small and their inner workings are transparent. We thus provide formal proofs that their perfect accuracy holds not only on a given test set, but for any input sequence.

Abstract (translated)

URL

https://arxiv.org/abs/2111.00600

PDF

https://arxiv.org/pdf/2111.00600.pdf


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