Papers › Minimum Description Length Recurrent Neural Networks
Minimum Description Length Recurrent Neural Networks
Nur Lan, Michal Geyer, Emmanuel Chemla, Roni Katzir
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 optimizing this objective function master tasks involving memory challenges and go beyond context-free languages. These learners master languages such as aⁿbⁿ, aⁿbⁿcⁿ, aⁿb²ⁿ, aⁿbᵐcⁿ⁺ᵐ, and they perform addition. Moreover, they often do so with 100% accuracy. The networks are 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. To our knowledge, no other connectionist model has been shown to capture the underlying grammars for these languages in full generality.
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