4 papers
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…
The challenge of reproducible ML: an empirical study on the impact of bugs
Emilio Rivera-Landos, Foutse Khomh, Amin Nikanjam
Reproducibility is a crucial requirement in scientific research. When results of research studies and scientific papers have been found difficult or impossible to reproduce, we fac…
Design Smells in Deep Learning Programs: An Empirical Study
Amin Nikanjam, Foutse Khomh
Nowadays, we are witnessing an increasing adoption of Deep Learning (DL) based software systems in many industries. Designing a DL program requires constructing a deep neural netwo…
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
Amin Nikanjam, Houssem Ben Braiek, Mohammad Mehdi Morovati +1
Nowadays, we are witnessing an increasing demand in both corporates and academia for exploiting Deep Learning (DL) to solve complex real-world problems. A DL program encodes the ne…