Marina Marie-Claire Höhne (née Vidovic)
Marina Marie-Claire Höhne (née Vidovic)
Full Professor at Uni Potsdam, Head of the Data Science department at ATB-Potsdam
Verified email at
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Improving the robustness of myoelectric pattern recognition for upper limb prostheses by covariate shift adaptation
MMC Vidovic, HJ Hwang, S Amsüss, JM Hahne, D Farina, KR Müller
IEEE Transactions on Neural Systems and Rehabilitation Engineering 24 (9 …, 2015
Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
A Hedström, L Weber, D Krakowczyk, D Bareeva, F Motzkus, W Samek, ...
Journal of Machine Learning Research 24 (34), 1-11, 2023
Feature importance measure for non-linear learning algorithms
MMC Vidovic, N Görnitz, KR Müller, M Kloft
arXiv preprint arXiv:1611.07567, 2016
This looks more like that: Enhancing self-explaining models by prototypical relevance propagation
S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer
Pattern Recognition 136, 109172, 2023
DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies
B Mieth, A Rozier, JA Rodriguez, MMC Höhne, N Görnitz, KR Müller
NAR genomics and bioinformatics 3 (3), lqab065, 2021
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data
B Mieth, JRF Hockley, N Görnitz, MMC Vidovic, KR Müller, A Gutteridge, ...
Scientific reports 9 (1), 20353, 2019
Noisegrad—enhancing explanations by introducing stochasticity to model weights
K Bykov, A Hedström, S Nakajima, MMC Höhne
Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6132-6140, 2022
How Much Can I Trust You?--Quantifying Uncertainties in Explaining Neural Networks
K Bykov, MMC Höhne, KR Müller, S Nakajima, M Kloft
arXiv preprint arXiv:2006.09000, 2020
Protovae: A trustworthy self-explainable prototypical variational model
S Gautam, A Boubekki, S Hansen, S Salahuddin, R Jenssen, M Höhne, ...
Advances in Neural Information Processing Systems 35, 17940-17952, 2022
Explaining bayesian neural networks
K Bykov, MMC Höhne, A Creosteanu, KR Müller, F Klauschen, ...
arXiv preprint arXiv:2108.10346, 2021
Covariate shift adaptation in EMG pattern recognition for prosthetic device control
MMC Vidovic, LP Paredes, HJ Hwang, S Amsu, J Pahl, JM Hahne, ...
2014 36th annual international conference of the IEEE engineering in …, 2014
Finding the right XAI method—a guide for the evaluation and ranking of explainable AI methods in climate science
PL Bommer, M Kretschmer, A Hedström, D Bareeva, MMC Höhne
Artificial Intelligence for the Earth Systems 3 (3), e230074, 2024
The meta-evaluation problem in explainable AI: identifying reliable estimators with MetaQuantus
A Hedström, P Bommer, KK Wickstrøm, W Samek, S Lapuschkin, ...
arXiv preprint arXiv:2302.07265, 2023
Opening the black box: Revealing interpretable sequence motifs in kernel-based learning algorithms
MMC Vidovic, N Görnitz, KR Müller, G Rätsch, M Kloft
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015
DORA: Exploring outlier representations in deep neural networks
K Bykov, M Deb, D Grinwald, KR Müller, MMC Höhne
arXiv preprint arXiv:2206.04530, 2022
SVM2Motif—reconstructing overlapping DNA sequence motifs by mimicking an SVM predictor
MMC Vidovic, N Görnitz, KR Müller, G Rätsch, M Kloft
PloS one 10 (12), e0144782, 2015
Demonstrating the risk of imbalanced datasets in chest x-ray image-based diagnostics by prototypical relevance propagation
S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer
2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-5, 2022
How much can I trust you
K Bykov, MMC Höhne, KR Müller, S Nakajima, M Kloft
Quantifying Uncertainties in Explaining Neural Networks, 2020
ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines
MMC Vidovic, M Kloft, KR Mueller, N Goernitz
PloS one 12 (3), e0174392, 2017
Labeling neural representations with inverse recognition
K Bykov, L Kopf, S Nakajima, M Kloft, M Höhne
Advances in Neural Information Processing Systems 36, 2024
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