91 citations · 250 across the 37 of their papers we have counts for
7 papers · 1 filter
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow
Florian Tambon, Amin Nikanjam, Le An +2
Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However,…
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods
Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh +1
The increasing adoption of Reinforcement Learning in safety-critical systems domains such as autonomous vehicles, health, and aviation raises the need for ensuring their safety. Ex…
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…
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
Florian Tambon, Gabriel Laberge, Le An +7
Context: Machine Learning (ML) has been at the heart of many innovations over the past years. However, including it in so-called 'safety-critical' systems such as automotive or aer…
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…
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
Amin Nikanjam, Mohammad Mehdi Morovati, Foutse Khomh +1
A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is t…