1 citations · 3 across the 6 of their papers we have counts for
6 papers
Formal Abductive Latent Explanations for Prototype-Based Networks
Jules Soria, Zakaria Chihani, Julien Girard-Satabin +3
Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes.…
The CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
Michele Alberti, François Bobot, Julien Girard-Satabin +3
The formal specification and verification of machine learning programs saw remarkable progress in less than a decade, leading to a profusion of tools. However, diversity may lead t…
Neural Network Verification is a Programming Language Challenge
Lucas C. Cordeiro, Matthew L. Daggitt, Julien Girard-Satabin +8
Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while pr…
Contextualised Out-of-Distribution Detection using Pattern Identication
Romain Xu-Darme, Julien Girard-Satabin, Darryl Hond +2
In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring patterns to build a robust Out-of-Distributio…
Interpretable Out-Of-Distribution Detection Using Pattern Identification
Romain Xu-Darme, Julien Girard-Satabin, Darryl Hond +2
Out-of-distribution (OoD) detection for data-based programs is a goal of paramount importance. Common approaches in the literature tend to train detectors requiring inside-of-distr…
DISCO Verification: Division of Input Space into COnvex polytopes for neural network verification
Julien Girard-Satabin, Aymeric Varasse, Marc Schoenauer +2
The impressive results of modern neural networks partly come from their non linear behaviour. Unfortunately, this property makes it very difficult to apply formal verification tool…