103 citations · 201 across the 29 of their papers we have counts for
7 papers · 1 filter
Explainability as statistical inference
Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen +1
A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and ca…
Generalised Mutual Information for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +4
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regul…
A Multi-stage deep architecture for summary generation of soccer videos
Melissa Sanabria, Frédéric Precioso, Pierre-Alexandre Mattei +1
Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in…
Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn +5
We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sens…
Asteroid Taxonomy from Cluster Analysis of Spectrometry and Albedo
Max Mahlke, Benoit Carry, Pierre-Alexandre Mattei
The classification of the minor bodies of the Solar System based on observables has been continuously developed and iterated over the past 40 years. While prior iterations followed…
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei
Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount o…
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives
Pierre-Alexandre Mattei, Jes Frellsen
We revisit the theory of importance weighted variational inference (IWVI), a promising strategy for learning latent variable models. IWVI uses new variational bounds, known as Mont…