PAUL TEMPLE

Belgium

Source: Scopus
Calculated based on no. of publications stored in Pure and citations from Scopus

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1 Active

VeriLearn: Verifying Learning Artificial Intelligence Systems
HEYMANS, P., FRENAY, B., SCHOBBENS, P., TEMPLE, P., Nanfack, G., Amrani, M. & Bibal, A.
1/01/18 → 31/12/22
Project: Research

5 Citations
1 h-Index
3 Conference contribution
2 Article
1 Paper

Empirical Assessment of Generating Adversarial Configurations for Software Product Lines
TEMPLE, PAUL., PERROUIN, GILLES., ACHER, M., Biggio, B., Jézéquel, J-M. & Roli, F., 2020, (Accepted/In press) In : Empirical Software Engineering . 57 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File
4 Downloads (Pure)
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Delobelle, P., Temple, P., Perrouin, G., FRENAY, BENOIT., Heymans, P. & Berendt, B., 14 May 2020, 1st workshop on Bias and Fairness in AI, co-located with ECMLPKDD 2020. 15 p.
Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

Open Access
File
14 Downloads (Pure)
Customizing Adversarial Machine Learning to test Deep Learning techniques
Temple, P., Perrouin, G., Frenay, B. & Schobbens, P-Y., 28 May 2019.
Research output: Contribution to conference › Paper › peer-review

Open Access
File

148 Downloads (Pure)
Empirical Assessment of Multimorphic Testing
Temple, P., Acher, M. & Jézéquel, J-M., 6 Jul 2019, (Accepted/In press) In : IEEE Transactions on Software Engineering.
Research output: Contribution to journal › Article › peer-review

File

20 Downloads (Pure)
Towards learning-aided configuration in 3D printing: Feasibility study and application to defect prediction
Amand, B., Cordy, M., Heymans, P., Acher, M., Temple, P. & Jézéquel, J. M., 6 Feb 2019, Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019. ACM Press, (ACM International Conference Proceeding Series).
Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

VeriLearn: Verifying Learning Artificial Intelligence Systems

Empirical Assessment of Generating Adversarial Configurations for Software Product Lines

Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning

Customizing Adversarial Machine Learning to test Deep Learning techniques

Empirical Assessment of Multimorphic Testing

Towards learning-aided configuration in 3D printing: Feasibility study and application to defect prediction