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Verification for Object Detection -- IBP IoU
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric.
Surrogate Neural Networks Local Stability for Aircraft Predictive Maintenance
Surrogate Neural Networks (NN) now routinely serve as substitutes for computationally demanding simulations (e. g., finite element).
Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing
As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety.
A Model-Agnostic SAT-based Approach for Symbolic Explanation Enumeration
In this paper titled A Model-Agnostic SAT-based approach for Symbolic Explanation Enumeration we propose a generic agnostic approach allowing to generate different and complementary types of symbolic explanations.
ASTERYX : A model-Agnostic SaT-basEd appRoach for sYmbolic and score-based eXplanations
The ever increasing complexity of machine learning techniques used more and more in practice, gives rise to the need to explain the predictions and decisions of these models, often used as black-boxes.
A Symbolic Approach for Counterfactual Explanations
In this paper titled A Symbolic Approach for Counterfactual Explanations we propose a novel symbolic approach to provide counterfactual explanations for a classifier predictions.
