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LORENTZ Joe

Main Referenced Co-authors
AOUADA, Djamila  (4)
Hartmann, Thomas (3)
Moawad, Assaad (3)
FOUQUET, François  (1)
Fouquet, Francois (1)
Main Referenced Keywords
Computational graph model (1); computer vision (1); defect detection (1); Differentiable programming (1); Edge AI (1);
Main Referenced Disciplines
Computer science (4)

Publications (total 4)

The most downloaded
264 downloads
Lorentz, J., Hartmann, T., Moawad, A., Fouquet, F., & Aouada, D. (2021). Explaining Defect Detection with Saliency Maps. In 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II (pp. 506-518). Cham, Switzerland: Springer. doi:10.1007/978-3-030-79463-7_43 https://hdl.handle.net/10993/47848

The most cited

6 citations (Scopus®)

Lorentz, J., Hartmann, T., Moawad, A., Fouquet, F., & Aouada, D. (2021). Explaining Defect Detection with Saliency Maps. In 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II (pp. 506-518). Cham, Switzerland: Springer. doi:10.1007/978-3-030-79463-7_43 https://hdl.handle.net/10993/47848

Lorentz, J., Hartmann, T., Moawad, A., Fouquet, F., Aouada, D., & Le Traon, Y. (2022). CalcGraph: taming the high costs of deep learning using models. Software and Systems Modeling. doi:10.1007/s10270-022-01052-7
Peer Reviewed verified by ORBi

Lorentz, J., Hartmann, T., Moawad, A., & Aouada, D. (2022). Profiling the real world potential of neural network compression. In 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), Barcelona 1-3 August 2022. IEEE. doi:10.1109/COINS54846.2022.9854973
Peer reviewed

Lorentz, J., Hartmann, T., Moawad, A., Fouquet, F., & Aouada, D. (2021). Explaining Defect Detection with Saliency Maps. In 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021, Proceedings, Part II (pp. 506-518). Cham, Switzerland: Springer. doi:10.1007/978-3-030-79463-7_43
Peer reviewed

Lorentz, J., Hartmann, T., Moawad, A., & Aouada, D. (n.d.). Industrial defect detection on the edge with deep learning over scarcely labeled and extremely imbalanced data. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/54839.

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