10 citations · 19 across the 11 of their papers we have counts for
3 papers · 1 filter
A Framework to Learn with Interpretation
Jayneel Parekh, Pavlo Mozharovskyi, Florence d'Alché-Buc
To tackle interpretability in deep learning, we present a novel framework to jointly learn a predictive model and its associated interpretation model. The interpreter provides both…
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach
Valérie Beaudouin, Isabelle Bloch, David Bounie +6
The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits…
Speech-to-Singing Conversion in an Encoder-Decoder Framework
Jayneel Parekh, Preeti Rao, Yi-Hsuan Yang
In this paper our goal is to convert a set of spoken lines into sung ones. Unlike previous signal processing based methods, we take a learning based approach to the problem. This a…