Simon Klüttermann

Orcid: 0000-0001-9698-4339

According to our database1, Simon Klüttermann authored at least 13 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics.
CoRR, 2024

About Test-time training for outlier detection.
CoRR, 2024

On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification.
CoRR, 2024

Benchmarking Trust: A Metric for Trustworthy Machine Learning.
Proceedings of the Explainable Artificial Intelligence, 2024

On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2024

Autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms.
Proceedings of the International Joint Conference on Neural Networks, 2024

The Phenomenon of Correlated Representations in Contrastive Learning.
Proceedings of the International Joint Conference on Neural Networks, 2024

Evaluating Anomaly Detection Algorithms: A Multi-Metric Analysis Across Variable Class Imbalances.
Proceedings of the International Joint Conference on Neural Networks, 2024

A Laser-based Volumetric Measurement Approach for Industrial Settings.
Proceedings of the 20th IEEE International Conference on Automation Science and Engineering, 2024

2023
Evaluating and Comparing Heterogeneous Ensemble Methods for Unsupervised Anomaly Detection.
Proceedings of the International Joint Conference on Neural Networks, 2023

On Graph Representation based Re-Identification - A Proof of Concept.
Proceedings of the IEEE International Conference on Data Mining, 2023

2022
Towards Graph Representation based Re-Identification of Chipwood Pallet Blocks.
Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Post-Robustifying Deep Anomaly Detection Ensembles by Model Selection.
Proceedings of the IEEE International Conference on Data Mining, 2022


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