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Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach
Moreover, we show how the process provides interesting insights toward the behavior of RNN learned on data, enlarging the scope of this work to the one of explainability of deep learning models.
Learning with Partially Ordered Representations
This paper examines the characterization and learning of grammars defined with enriched representational models.
Explaining Black Boxes on Sequential Data using Weighted Automata
Understanding how a learned black box works is of crucial interest for the future of Machine Learning.
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