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2020 – today
- 2024
[c6]Thorben Markmann, Michiel Straat, Barbara Hammer:
Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection. IJCNN 2024: 1-8- [i7]Thorben Markmann, Michiel Straat, Barbara Hammer:
Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection. CoRR abs/2405.06425 (2024) - 2023
- [c5]Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl:
Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis. ESANN 2023 - 2022
- [j3]Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert
, Barbara Hammer, Michael Biehl:
Supervised learning in the presence of concept drift: a modelling framework. Neural Comput. Appl. 34(1): 101-118 (2022) - [c4]Michiel Straat, Kevin Koster, Nick Goet, Kerstin Bunte:
An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data. IJCNN 2022: 1-8 - [i6]Michiel Straat, Kevin Koster, Nick Goet, Kerstin Bunte:
An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data. CoRR abs/2206.05818 (2022) - 2020
- [j2]Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert:
Learning vector quantization and relevances in complex coefficient space. Neural Comput. Appl. 32(24): 18085-18099 (2020) - [c3]Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif:
Complex-Valued Embeddings of Generic Proximity Data. S+SSPR 2020: 14-23 - [i5]Michiel Straat, Fthi Abadi, Zhuoyun Kan, Christina Göpfert, Barbara Hammer, Michael Biehl:
Supervised Learning in the Presence of Concept Drift: A modelling framework. CoRR abs/2005.10531 (2020) - [i4]Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif:
Complex-valued embeddings of generic proximity data. CoRR abs/2008.13454 (2020)
2010 – 2019
- 2019
- [c2]Michiel Straat, Michael Biehl:
On-line learning dynamics of ReLU neural networks using statistical physics techniques. ESANN 2019 - [i3]Michiel Straat, Michael Biehl:
On-line learning dynamics of ReLU neural networks using statistical physics techniques. CoRR abs/1903.07378 (2019) - [i2]Michiel Straat, Jorrit Oosterhof:
Segmentation of blood vessels in retinal fundus images. CoRR abs/1905.12596 (2019) - [i1]Elisa Oostwal, Michiel Straat, Michael Biehl:
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation. CoRR abs/1910.07476 (2019) - 2018
- [j1]Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl:
Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy 20(10): 775 (2018) - 2017
- [c1]Michiel Straat, Marika Kaden, Matthias Gay, Thomas Villmann, Alexander Lampe, Udo Seiffert, Michael Biehl, Friedrich Melchert:
Prototypes and matrix relevance learning in complex fourier space. WSOM 2017: 139-144
Coauthor Index
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