97 citations · 109 across the 6 of their papers we have counts for
6 papers
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…
Towards Using Probabilistic Models to Design Software Systems with Inherent Uncertainty
Alex Serban, Erik Poll, Joost Visser
The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and…
Adoption and Effects of Software Engineering Best Practices in Machine Learning
Alex Serban, Koen van der Blom, Holger Hoos +1
The increasing reliance on applications with machine learning (ML) components calls for mature engineering techniques that ensure these are built in a robust and future-proof manne…
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks
Steven Carr, Nils Jansen, Ralf Wimmer +3
We study strategy synthesis for partially observable Markov decision processes (POMDPs). The particular problem is to determine strategies that provably adhere to (probabilistic) t…