7 citations · 7 across the 6 of their papers we have counts for
6 papers
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi, Ahmed Haj yahmed, Mohamed Elshafei +2
Deep Learning (DL) systems are increasingly deployed in safety-critical applications, yet they remain vulnerable to robustness issues that can lead to significant failures. While n…
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil +4
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-ba…
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek +1
Deep reinforcement learning (DRL) is increasingly applied in large-scale productions like Netflix and Facebook. As with most data-driven systems, DRL systems can exhibit undesirabl…
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam +2
Deep reinforcement learning (DRL), leveraging Deep Learning (DL) in reinforcement learning, has shown significant potential in achieving human-level autonomy in a wide range of dom…
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…
An Empirical Study of Challenges in Converting Deep Learning Models
Moses Openja, Amin Nikanjam, Ahmed Haj Yahmed +3
There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually DL models are developed and trained using DL frameworks that have th…