Johannes S. Otterbach

According to our database, Johannes S. Otterbach authored at least 13 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

Self-Distilled Representation Learning for Time Series.

Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes S. Otterbach

CoRR, 2023

Scaling Experiments in Self-Supervised Cross-Table Representation Learning.

Maximilian Schambach, Dominique Paul, Johannes S. Otterbach

CoRR, 2023

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management.

Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the IFIP Networking Conference, 2023

Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation.

Alexander Koenig, Maximilian Schambach, Johannes S. Otterbach

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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

Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks.

Winfried Lötzsch, Simon Ohler, Johannes S. Otterbach

CoRR, 2022

Auto-Compressing Subset Pruning for Semantic Image Segmentation.

Konstantin Ditschuneit, Johannes S. Otterbach

Proceedings of the Pattern Recognition, 2022

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

MEAL: Manifold Embedding-based Active Learning.

Deepthi Sreenivasaiah, Johannes S. Otterbach, Thomas Wollmann

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs.

Johannes S. Otterbach, Thomas Wollmann

Proceedings of the 51. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2021 - Computer Science & Sustainability, Berlin, Germany, 27. September, 2021

2018

Optimizing Variational Quantum Circuits using Evolution Strategies.

Johannes S. Otterbach

CoRR, 2018