7 citations · 10 across the 6 of their papers we have counts for
6 papers
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
Altaf Allah Abbassi, Houssem Ben Braiek, Foutse Khomh +1
The industry increasingly relies on deep learning (DL) technology for manufacturing inspections, which are challenging to automate with rule-based machine vision algorithms. DL-pow…
Machine Learning Robustness: A Primer
Houssem Ben Braiek, Foutse Khomh
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems.…
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Houssem Ben Braiek, Ali Tfaily, Foutse Khomh +2
Aircraft industry is constantly striving for more efficient design optimization methods in terms of human efforts, computation time, and resource consumption. Hybrid surrogate opti…
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
Houssem Ben Braiek, Thomas Reid, Foutse Khomh
In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowled…
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse Khomh +2
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to…
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases
Ettore Merlo, Mira Marhaba, Foutse Khomh +2
Neural network test cases are meant to exercise different reasoning paths in an architecture and used to validate the prediction outcomes. In this paper, we introduce "computationa…