Maximilian Alber
Maximilian Alber
Verified email at tu-berlin.de - Homepage
TitleCited byYear
The (un) reliability of saliency methods
PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ...
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267-280, 2019
762019
Learning how to explain neural networks: Patternnet and patternattribution
PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne
arXiv preprint arXiv:1705.05598, 2017
672017
Patternnet and patternlrp–improving the interpretability of neural networks
PJ Kindermans, KT Schütt, M Alber, KR Müller, S Dähne
stat 1050, 16, 2017
252017
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
Journal of Machine Learning Research 20 (93), 1-8, 2019
232019
Backprop evolution
M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le
arXiv preprint arXiv:1808.02822, 2018
52018
Distributed optimization of multi-class SVMs
M Alber, J Zimmert, U Dogan, M Kloft
PloS one 12 (6), e0178161, 2017
32017
An empirical study on the properties of random bases for kernel methods
M Alber, PJ Kindermans, K Schütt, KR Müller, F Sha
Advances in Neural Information Processing Systems, 2763-2774, 2017
32017
Explanations can be manipulated and geometry is to blame
AK Dombrowski, M Alber, CJ Anders, M Ackermann, KR Müller, P Kessel
arXiv preprint arXiv:1906.07983, 2019
22019
Software and application patterns for explanation methods
M Alber
arXiv preprint arXiv:1904.04734, 2019
12019
How to iNNvestigate neural networks' predictions!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
12018
Masterarbeit: Big Data and Machine Learning: A Case Study with Bump Boost
M Alber
2015
The (Un) reliability of Saliency Methods
M Alber, KT Schütt, S Dähne, D Erhan, B Kim
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267, 0
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Articles 1–12