Hugo Gangloff (Université Bretagne Sud, Laboratoire IRISA)

Deep Pairwise and Triplet Markov Chains for unsupervised signal processing

vendredi 25 février 2022, 10h00

Salle du conseil, espace Turing

Probabilistic graphical models such as hidden Markov models have found many applications in unsupervised signal processing, such as part-of-speech tagging, image segmentation, genetic sequence analysis, etc. In this presentation, we focus on the pairwise and triplet Markov chain models which define very general frameworks that have been very little explored so far. While pairwise Markov chains strictly extend the direct dependencies that can be introduced by the model, triplet Markov chains additionally enable the introduction of much more complex probability distributions. However, such generalizations raise the questions of the choice of the probability distributions, their parametrization and the unsupervised parameter estimation in the complex models that can be built. We will explore these questions and propose answers: i) we use an auxiliary latent process to implicitly define complex probability distributions, ii) the parametrization issue is considered by embedding deep neural networks in the new models and iii) a general algorithm, based on a lower bound of the loglikelihood, is derived in order to perform unsupervised parameter estimation in these sequential models,. We show that the new models outperform the hidden Markov chains and their classical extensions usually considered in the literature.