1) Apprentissage profond et factorisation de matrices : article CAP 2022 (Vannes)

Tensor Decomposition for Multi-Target Deep-Learning in the context of Predictive Justice

Alexandre AUDIBERT⋆, Konstantin USEVICH†, Massih-Reza AMINI⋆, and Marianne CLAUSEL

Résumé :

In recent years, deep learning (DL) models for information retrieval have attracted a lot of attention. These
models are data-hungry, necessitating large-scale training samples for learning, particularly when the goal
is to associate documents with heterogeneous outputs (continuous and discrete); they also lack interpretation.
In this paper, we propose to apply tensor decomposition on a DL model to learn with heterogeneous out-
puts in the context of predictive justice. This strategy makes sense in terms of model interpretation, and it
allows us to reduce some layers by a factor of ten without sacrificing performance on the European Court of
Human Rights collection.