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Openja, M., Nikanjam, A., Yahmed, A. H., Khomh, F., & Jiang, Z. M. J. (octobre 2022). An Empirical Study of Challenges in Converting Deep Learning Models [Communication écrite]. 39th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022), Limassol, Cyprus. Lien externe
Yahmed, A. H., Allah Abbassi, A., Nikanjam, A., Li, H., & Khomh, F. (octobre 2023). Deploying deep reinforcement learning systems: a taxonomy of challenges [Communication écrite]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2023), Bogota, Colombia. Lien externe
Yahmed, A. H., Bouchoucha, R., Ben Braiek, H., & Khomh, F. (septembre 2023). An Intentional Forgetting-Driven Self-Healing Method for Deep Reinforcement Learning Systems [Communication écrite]. 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023), Echternach, Luxembourg. Lien externe
Yahmed, A. H., Ben Braiek, H., Khomh, F., Bouzidi, S., & Zaatour, R. (2022). DiverGet: a Search-Based Software Testing approach for Deep Neural Network Quantization assessment. Empirical Software Engineering, 27(7), 193 (32 pages). Lien externe