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ORCIDJohannes S. Otterbach
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2020 – today
- 2024
[j1]Olga Kondrateva, Stefan Dietzel, Maximilian Schambach, Johannes S. Otterbach, Björn Scheuermann:
Progressive updates of convolutional neural networks for enhanced reliability in small satellite applications. Comput. Commun. 225: 185-194 (2024)- 2023
- [c6]Alexander Koenig, Maximilian Schambach
, Johannes S. Otterbach:
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation. CVPR Workshops 2023: 3789-3798 - [c5]Olga Kondrateva, Stefan Dietzel, Maximilian Schambach, Johannes S. Otterbach, Björn Scheuermann:
Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization. IFIP Networking 2023: 1-9 - [c4]Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes S. Otterbach, Martin Genzel:
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. NeurIPS 2023 - [i11]Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach:
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management. CoRR abs/2303.10382 (2023) - [i10]Alexander Koenig, Maximilian Schambach, Johannes S. Otterbach:
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation. CoRR abs/2304.07314 (2023) - [i9]Julien Siems, Konstantin Ditschuneit, Winfried Ripken, Alma Lindborg, Maximilian Schambach, Johannes S. Otterbach, Martin Genzel:
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models. CoRR abs/2305.11475 (2023) - [i8]Maximilian Schambach, Dominique Paul, Johannes S. Otterbach:
Scaling Experiments in Self-Supervised Cross-Table Representation Learning. CoRR abs/2309.17339 (2023) - [i7]Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes S. Otterbach:
Self-Distilled Representation Learning for Time Series. CoRR abs/2311.11335 (2023) - 2022
- [c3]Konstantin Ditschuneit, Johannes S. Otterbach:
Auto-Compressing Subset Pruning for Semantic Image Segmentation. GCPR 2022: 20-35 - [i6]Konstantin Ditschuneit, Johannes S. Otterbach:
Auto-Compressing Subset Pruning for Semantic Image Segmentation. CoRR abs/2201.11103 (2022) - [i5]Winfried Lötzsch, Simon Ohler, Johannes S. Otterbach:
Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks. CoRR abs/2206.14092 (2022) - [i4]Simon Ohler, Daniel Brady, Winfried Lötzsch, Michael Fleischhauer, Johannes S. Otterbach:
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks. CoRR abs/2207.08927 (2022) - 2021
- [c2]Johannes S. Otterbach, Thomas Wollmann:
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs. GI-Jahrestagung 2021: 1185-1191 - [c1]Deepthi Sreenivasaiah, Johannes S. Otterbach, Thomas Wollmann:
MEAL: Manifold Embedding-based Active Learning. ICCVW 2021: 1029-1037 - [i3]Johannes S. Otterbach, Thomas Wollmann:
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs. CoRR abs/2105.03669 (2021) - [i2]Samuel von Baußnern, Johannes S. Otterbach, Adrian Loy, Mathieu Salzmann, Thomas Wollmann:
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows. CoRR abs/2105.14638 (2021)
2010 – 2019
- 2018
- [i1]Johannes S. Otterbach:
Optimizing Variational Quantum Circuits using Evolution Strategies. CoRR abs/1806.04344 (2018)
Coauthor Index
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last updated on 2024-08-10 01:24 CEST by the dblp team
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