| 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 | 195 | 2023 |
| 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 | 191 | 2015 |
| 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 | 44 | 2023 |
| Feature importance measure for non-linear learning algorithms MMC Vidovic, N Görnitz, KR Müller, M Kloft arXiv preprint arXiv:1611.07567, 2016 | 41 | 2016 |
| 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 | 39 | 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 | 35 | 2019 |
| 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 | 34 | 2022 |
| 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 | 34 | 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 | 34 | 2020 |
| Explaining bayesian neural networks K Bykov, MMC Höhne, A Creosteanu, KR Müller, F Klauschen, ... arXiv preprint arXiv:2108.10346, 2021 | 27 | 2021 |
| 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 | 26 | 2024 |
| 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 | 21 | 2014 |
| 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 | 20 | 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 | 17 | 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 | 14 | 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 | 11 | 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 | 10 | 2022 |
| Labeling neural representations with inverse recognition K Bykov, L Kopf, S Nakajima, M Kloft, M Höhne Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| 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 | 9 | 2021 |
| How much can I trust you K Bykov, MMC Höhne, KR Müller, S Nakajima, M Kloft Quantifying Uncertainties in Explaining Neural Networks, 2020 | 9 | 2020 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
