97 citations · 170 across the 10 of their papers we have counts for
11 papers
Deep Repulsive Prototypes for Adversarial Robustness
Alex Serban, Erik Poll, Joost Visser
While many defences against adversarial examples have been proposed, finding robust machine learning models is still an open problem. The most compelling defence to date is adversa…
Adapting Software Architectures to Machine Learning Challenges
Alex Serban, Joost Visser
Unique developmental and operational characteristics of ML components as well as their inherent uncertainty demand robust engineering principles are used to ensure their quality. W…
Practices for Engineering Trustworthy Machine Learning Applications
Alex Serban, Koen van der Blom, Holger Hoos +1
Following the recent surge in adoption of machine learning (ML), the negative impact that improper use of ML can have on users and society is now also widely recognised. To address…
Adversarial Examples on Object Recognition: A Comprehensive Survey
Alex Serban, Erik Poll, Joost Visser
Deep neural networks are at the forefront of machine learning research. However, despite achieving impressive performance on complex tasks, they can be very sensitive: Small pertur…
GraphRepo: Fast Exploration in Software Repository Mining
Alex Serban, Magiel Bruntink, Joost Visser
Mining and storage of data from software repositories is typically done on a per-project basis, where each project uses a unique combination of data schema, extraction tools, and (…
Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise
Alex Serban, Erik Poll, Joost Visser
Sensitivity to adversarial noise hinders deployment of machine learning algorithms in security-critical applications. Although many adversarial defenses have been proposed, robustn…