no code implementations • 27 Mar 2024 • Dennis Gross, Helge Spieker
We introduce a method to verify stochastic reinforcement learning (RL) policies.
no code implementations • 27 Mar 2024 • Dennis Gross, Helge Spieker, Arnaud Gotlieb, Ricardo Knoblauch
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case.
no code implementations • 25 Mar 2024 • Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility.
no code implementations • 24 Oct 2023 • Pierre Bernabé, Arnaud Gotlieb, Bruno Legeard, Dusica Marijan, Frank Olaf Sem-Jacobsen, Helge Spieker
In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration.
1 code implementation • 24 Aug 2023 • Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar, Helge Spieker
The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods.
no code implementations • 24 Feb 2022 • Mohit Kumar Ahuja, Arnaud Gotlieb, Helge Spieker
To discover faults in DL models, existing software testing methods have been adapted and refined accordingly.
no code implementations • 4 Nov 2021 • Helge Spieker, Arnaud Gotlieb
Solving Constraint Optimization Problems (COPs) can be dramatically simplified by boundary estimation, that is, providing tight boundaries of cost functions.
no code implementations • 29 Sep 2021 • Mohit Kumar Ahuja, Sahil Sahil, Helge Spieker
Image classification with classes of varying difficulty can cause performance disparity in deep learning models and reduce the overall performance and reliability of the predictions.
1 code implementation • 24 Apr 2021 • Helge Spieker
Still, training the agents is data-intensive and there are no guarantees that the learned behavior is safe and does not violate rules of the environment, which has limitations for the practical deployment in real-world scenarios.
1 code implementation • 12 Nov 2020 • Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristof Szabados, Helge Spieker, Matej Madeja, Nathaniel Hoy, Valentina Lenarduzzi, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh, Yihao Qin, Debasish Chakroborti, Willard Davis, Vijay Walunj, Hongjun Wu, Diego Marcilio, Omar Alam, Abdullah Aldaeej, Idan Amit, Burak Turhan, Simon Eismann, Anna-Katharina Wickert, Ivano Malavolta, Matus Sulir, Fatemeh Fard, Austin Z. Henley, Stratos Kourtzanidis, Eray Tuzun, Christoph Treude, Simin Maleki Shamasbi, Ivan Pashchenko, Marvin Wyrich, James Davis, Alexander Serebrenik, Ella Albrecht, Ethem Utku Aktas, Daniel Strüber, Johannes Erbel
Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits.
Software Engineering
no code implementations • 14 Jul 2020 • Mohit Kumar Ahuja, Mohamed-Bachir Belaid, Pierre Bernabé, Mathieu Collet, Arnaud Gotlieb, Chhagan Lal, Dusica Marijan, Sagar Sen, Aizaz Sharif, Helge Spieker
Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI).
no code implementations • 20 Jun 2020 • Helge Spieker, Arnaud Gotlieb
Constraint Optimization Problems (COP) are often considered without sufficient knowledge on the boundaries of the objective variable to optimize.
no code implementations • 12 Feb 2019 • Morten Mossige, Arnaud Gotlieb, Helge Spieker, Hein Meling, Mats Carlsson
When testing industrial robots, it is common that the target machines need to share some common resources, e. g., costly hardware devices, and so there is a need to schedule test case execution on the target machines, accounting for these shared resources.
no code implementations • 14 Jan 2019 • Helge Spieker, Arnaud Gotlieb
However, testing the training routines requires running them and fully training a deep learning model can be resource-intensive, when using the full data set.
2 code implementations • 9 Nov 2018 • Helge Spieker, Arnaud Gotlieb, Dusica Marijan, Morten Mossige
Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle.
1 code implementation • 9 Nov 2018 • Arnaud Gotlieb, Dusica Marijan, Helge Spieker
Constraint Programming (CP) is a powerful declarative programming paradigm where inference and search are interleaved to find feasible and optimal solutions to various type of constraint systems.
no code implementations • 8 Nov 2018 • Helge Spieker, Arnaud Gotlieb, Morten Mossige
Multi-cycle assignment problems address scenarios where a series of general assignment problems has to be solved sequentially.