This document describes concisely the ubiquitous class of exponential family distributions
met in statistics. The first part recalls definitions and summarizes main properties and duality …

In this survey, we describe the fundamental differential-geometric structures of information
manifolds, state the fundamental theorem of information geometry, and illustrate some use …

Agglomerative hierarchical clustering differs from partition-based clustering since it builds a
binary merge tree starting from leaves that contain data elements to the root that contains the …

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music
and specifically hymn-like pieces. We claim that, after being trained on the chorale …

… for portfolio selection (Richard Nock, Brice Magdalou, Eric Briys, and Frank Nielsen) • Learning
mixtures by simplifying kernel density estimators (Olivier Schwander and Frank Nielsen) …

Recent papers and patents in iterative unsupervised learning have emphasized a new trend
in clustering. It basically consists of penalizing solutions via weights on the instance points, …

This paper explores a statistical basis for a process often described in computer vision: image
segmentation by region merging following a particular order in the choice of regions. We …

The occurrence of hydronephrosis and hydroureters during pregnancy has been termed
physiological, inasmuch as it is seen in more than 80%, most frequently and most pronounced …

In this paper, we generalize the notions of centroids (and barycenters) to the broad class of
information-theoretic distortion measures called Bregman divergences. Bregman …

This gentle introduction to High Performance Computing (HPC) for Data Science using the
Message Passing Interface (MPI) standard has been designed as a first course for …