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2020 – today
- 2024
[c4]Theodor Felix Stoecker, Nico Hambauer, Patrick Zschech, Mathias Kraus:
IGANN Sparse: Bridging Sparsity and Interpretability with Non-Linear Insight. ECIS 2024- [c3]Mathias Kraus, Nico Hambauer, Kristina Müller, Pavlina Kröckel, Nalika Ulapane, Arno De Caigny, Koen W. De Bock, Kristof Coussement:
Coupling Neural Networks Between Clusters for Better Personalized Care. HICSS 2024: 3627-3636 - [i4]Theodor Stoecker, Nico Hambauer, Patrick Zschech
, Mathias Kraus:
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight. CoRR abs/2403.11363 (2024) - [i3]Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech:
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. CoRR abs/2409.14429 (2024) - 2022
- [c2]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. ECIS 2022 - [c1]Maximilian V. Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. Wirtschaftsinformatik 2022 - [i2]Maximilian Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. CoRR abs/2201.02028 (2022) - [i1]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. CoRR abs/2204.09123 (2022)
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