14 citations · 21 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an app…
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini +1
We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-…
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data
Pierre-Alexandre Mattei, Jes Frellsen
We consider the problem of handling missing data with deep latent variable models (DLVMs). First, we present a simple technique to train DLVMs when the training set contains missin…