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Marina Marie-Claire Höhne (née Vidovic)
Marina Marie-Claire Höhne (née Vidovic)
Leader of a junior research group on explainable AI at TU Berlin, Associate Professor at UiT
Geverifieerd e-mailadres voor tu-berlin.de
Titel
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Jaar
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
1392015
Feature importance measure for non-linear learning algorithms
MMC Vidovic, N Görnitz, KR Müller, M Kloft
arXiv preprint arXiv:1611.07567, 2016
362016
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), 1-14, 2019
202019
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
162020
Opening the black box: Revealing interpretable sequence motifs in kernel-based learning algorithms
MMC Vidovic, N Görnitz, KR Müller, G Rätsch, M Kloft
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
152015
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
142014
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
122015
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
112021
Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations
A Hedström, L Weber, D Bareeva, F Motzkus, W Samek, S Lapuschkin, ...
arXiv preprint arXiv:2202.06861, 2022
92022
Explaining bayesian neural networks
K Bykov, MMC Höhne, A Creosteanu, KR Müller, F Klauschen, ...
arXiv preprint arXiv:2108.10346, 2021
82021
ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines
MMC Vidovic, M Kloft, KR Mueller, N Goernitz
PloS one 12 (3), e0174392, 2017
72017
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
42022
This looks more like that: Enhancing self-explaining models by prototypical relevance propagation
S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer
arXiv preprint arXiv:2108.12204, 2021
32021
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
22022
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout
Y Ali, A Taleb, MMC Höhne, C Lippert
arXiv preprint arXiv:2109.14288, 2021
22021
Visualizing the diversity of representations learned by Bayesian neural networks
D Grinwald, K Bykov, S Nakajima, MMC Höhne
arXiv preprint arXiv:2201.10859, 2022
12022
DORA: Exploring outlier representations in Deep Neural Networks
K Bykov, M Deb, D Grinwald, KR Müller, MMC Höhne
arXiv preprint arXiv:2206.04530, 2022
2022
A comparison of explainable AI solutions to a climate change prediction task
PL Bommer, M Kretschmer, D Bareeva, K Aksoy, M Höhne
EGU22, 2022
2022
Nachvollziehbare Künstliche Intelligenz: Methoden, Chancen und Risiken
MMC Höhne
Datenschutz und Datensicherheit-DuD 45 (7), 453-456, 2021
2021
Improving and interpreting machine learning algorithms with applications
MMC Vidovic
PQDT-Global, 2017
2017
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Artikelen 1–20