Publications

Thorben Markmann, Michiel Straat, Barbara Hammer (2024). Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection. Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection.

PDF Code Project

Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl (2023). Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. Off-line Learning Analysis for Soft Committee Machines with GELU Activation.

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Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl (2023). Off-line Learning Analysis for Soft Committee Machines with GELU Activation. Off-line Learning Analysis for Soft Committee Machines with GELU Activation.

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Michiel Straat, Kevin Koster, Nick Goet, Kerstin Bunte (2022). An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data. An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data.

Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl (2021). Supervised Learning in the Presence of Concept Drift: A modelling framework. Supervised Learning in the Presence of Concept Drift: A modelling framework.

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Elisa Oostwal, Michiel Straat, Michael Biehl (2020). Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation. Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation.

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Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif (2020). Complex-valued embeddings of generic proximity data. Technical report: Complex-valued embeddings of generic proximity data.

Michiel Straat, Michael Biehl (2019). On-line learning dynamics of ReLU neural networks using statistical physics techniques. Proc. European Symposium on Artificial Neural Networks (ESANN) 2019, Bruges/Belgium.

Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert (2019). Learning Vector Quantization and relevances in complex coefficient space. Neural Computing and Applications.

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Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl (2018). Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy.

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Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert (2017). Prototypes and matrix relevance learning in complex Fourier space. 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

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Michiel Straat, Jorrit Oosterhof (2017). Segmentation of blood vessels in retinal fundus images. 14th Student Colloquium at RuG.