Optimizing Variational Quantum Circuits using Evolution Strategies. - Johannes S. Otterbach (CoRR, 2018)

Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs. - Johannes S. Otterbach; Thomas Wollmann (GI-Jahrestagung, 2021)

MEAL: Manifold Embedding-based Active Learning. - Deepthi Sreenivasaiah; Johannes S. Otterbach; Thomas Wollmann (ICCVW, 2021)

DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows. - Samuel von Baußnern; Johannes S. Otterbach; Adrian Loy; Mathieu Salzmann; Thomas Wollmann (CoRR, 2021)

Auto-Compressing Subset Pruning for Semantic Image Segmentation. - Konstantin Ditschuneit; Johannes S. Otterbach (GCPR, 2022)

Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks. - Winfried Lötzsch; Simon Ohler; Johannes S. Otterbach (CoRR, 2022)

Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks. - Simon Ohler; Daniel Brady; Winfried Lötzsch; Michael Fleischhauer; Johannes S. Otterbach (CoRR, 2022)

Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation. - Alexander Koenig; Maximilian Schambach; Johannes S. Otterbach (CVPR Workshops, 2023)

Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization. - Olga Kondrateva; Stefan Dietzel; Maximilian Schambach; Johannes S. Otterbach; Björn Scheuermann 001 (IFIP Networking, 2023)

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. - Julien Siems; Konstantin Ditschuneit; Winfried Ripken; Alma Lindborg; Maximilian Schambach; Johannes S. Otterbach; Martin Genzel (NeurIPS, 2023)

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management. - Julien Siems; Maximilian Schambach; Sebastian Schulze; Johannes S. Otterbach (CoRR, 2023)

Scaling Experiments in Self-Supervised Cross-Table Representation Learning. - Maximilian Schambach; Dominique Paul; Johannes S. Otterbach (CoRR, 2023)

Self-Distilled Representation Learning for Time Series. - Felix Pieper; Konstantin Ditschuneit; Martin Genzel; Alexandra Lindt; Johannes S. Otterbach (CoRR, 2023)

Progressive updates of convolutional neural networks for enhanced reliability in small satellite applications. - Olga Kondrateva; Stefan Dietzel; Maximilian Schambach; Johannes S. Otterbach; Björn Scheuermann 001 (Comput. Commun., 2024)