103 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR11 papers
- Université Côte d'AzurFR11 papers
- University of SienaIT8 papers
- Institut national de recherche en sciences et technologies du numériqueFR7 papers
- Laboratoire Jean-Alexandre DieudonnéFR6 papers
- University of FlorenceIT5 papers
- Institut de Biologie ValroseFR3 papers
- University of CambridgeGB3 papers
- Centre Inria d'Université Côte d'AzurFR2 papers
- Institut de Physique de NiceFR2 papers
- Mathématiques Appliquées à Paris 5FR2 papers
- Technical University of DenmarkDK2 papers
21 papers
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…
Extending Logic Explained Networks to Text Classification
Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero +3
Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions. However, these models have o…
Embedded Topics in the Stochastic Block Model
Rémi Boutin, Charles Bouveyron, Pierre Latouche
Communication networks such as emails or social networks are now ubiquitous and their analysis has become a strategic field. In many applications, the goal is to automatically extr…
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…
Autoregressive based Drift Detection Method
Mansour Zoubeirou A Mayaki, Michel Riveill
In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution does not change over ti…
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
Enrico Meloni, Matteo Tiezzi, Luca Pasqualini +2
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in r…
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
Yuqi Gu, Elena A. Erosheva, Gongjun Xu +1
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs in…
Graph Neural Networks for Graph Drawing
Matteo Tiezzi, Gabriele Ciravegna, Marco Gori
Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiabl…
Logic Explained Networks
Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini +4
The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-…
Entropy-based Logic Explanations of Neural Networks
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini +3
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have…
Tensor decomposition for learning Gaussian mixtures from moments
Rima Khouja, Pierre-Alexandre Mattei, Bernard Mourrain
In data processing and machine learning, an important challenge is to recover and exploit models that can represent accurately the data. We consider the problem of recovering Gauss…
Unsupervised classification of SDSS galaxy spectra
Didier Fraix-Burnet, C. Bouveyron, J. Moultaka
Defining templates of galaxy spectra is useful to quickly characterise new observations and organise databases from surveys. These templates are usually built from a pre-defined cl…
Online Graph Dictionary Learning
Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary +2
Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context…
An Optimal Control Approach to Learning in SIDARTHE Epidemic model
Andrea Zugarini, Enrico Meloni, Alessandro Betti +3
The COVID-19 outbreak has stimulated the interest in the proposal of novel epidemiological models to predict the course of the epidemic so as to help planning effective control str…
SAILenv: Learning in Virtual Visual Environments Made Simple
Enrico Meloni, Luca Pasqualini, Matteo Tiezzi +2
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create…
Wave Propagation of Visual Stimuli in Focus of Attention
Lapo Faggi, Alessandro Betti, Dario Zanca +2
Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visua…
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…
Greedy clustering of count data through a mixture of multinomial PCA
Nicolas Jouvin, Pierre Latouche, Charles Bouveyron +2
Count data is becoming more and more ubiquitous in a wide range of applications, with datasets growing both in size and in dimension. In this context, an increasing amount of work…
R-miss-tastic: a unified platform for missing values methods and workflows
Imke Mayer, Aude Sportisse, Julie Josse +2
Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and vi…
A Parsimonious Tour of Bayesian Model Uncertainty
Pierre-Alexandre Mattei
Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears easier and easier to try and fit many…