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Measuring the Robustness of ML Models Against Data Quality Issues in Industrial Time Series Data.

, , , , , , , , and . INDIN, page 1-8. IEEE, (2023)

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Addressing Randomness in Evaluation Protocols for Out-of-Distribution Detection., , and . CoRR, (2022)Out-of-Distribution Detection with Logical Reasoning., , and . WACV, page 2111-2120. IEEE, (2024)Measuring the Robustness of ML Models Against Data Quality Issues in Industrial Time Series Data., , , , , , , , and . INDIN, page 1-8. IEEE, (2023)Towards Smart Factory: Multi-Agent Integration on Industrial Standards for Service-oriented Communication and Semantic Data Exchange., , , , and . WOA, volume 2215 of CEUR Workshop Proceedings, page 124-132. CEUR-WS.org, (2018)Evaluating and Increasing Segmentation Robustness in CARLA., , and . SAFECOMP Workshops, volume 14182 of Lecture Notes in Computer Science, page 390-396. Springer, (2023)Towards Deep Anomaly Detection with Structured Knowledge Representations.. SAFECOMP Workshops, volume 14182 of Lecture Notes in Computer Science, page 382-389. Springer, (2023)PyTorch-OOD: A Library for Out-of-Distribution Detection based on PyTorch., , and . CVPR Workshops, page 4350-4359. IEEE, (2022)Multi-Class Hypersphere Anomaly Detection., , and . ICPR, page 2636-2642. IEEE, (2022)Multi-disciplinary Engineering of Production Systems - Challenges for Quality of Control Software., , and . SWQD, volume 338 of Lecture Notes in Business Information Processing, page 3-13. Springer, (2019)Supporting the Data Model Integrator in an Engineering Network by Automating Data Integration., , , , , and . INDIN, page 1229-1234. IEEE, (2019)