4 citations · 5 across the 5 of their papers we have counts for
4 papers · 1 filter
Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach
Remi Eyraud, Stephane Ayache
This paper is an attempt to bridge the gap between deep learning and grammatical inference. Indeed, it provides an algorithm to extract a (stochastic) formal language from any recu…
Partial Trace Regression and Low-Rank Kraus Decomposition
Hachem Kadri, Stéphane Ayache, Riikka Huusari +2
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more genera…
Deep Networks with Adaptive Nyström Approximation
Luc Giffon, Stéphane Ayache, Thierry Artières +1
Recent work has focused on combining kernel methods and deep learning to exploit the best of the two approaches. Here, we introduce a new architecture of neural networks in which w…
Explaining Black Boxes on Sequential Data using Weighted Automata
Stephane Ayache, Remi Eyraud, Noe Goudian
Understanding how a learned black box works is of crucial interest for the future of Machine Learning. In this paper, we pioneer the question of the global interpretability of lear…