Maximilian Alber
Maximilian Alber
Verified email at tu-berlin.de - Homepage
Title
Cited by
Cited by
Year
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
1672017
The (un) reliability of saliency methods
PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ...
arXiv preprint arXiv:1711.00867, 2017
1382017
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
J. Mach. Learn. Res. 20 (93), 1-8, 2019
912019
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
592019
Explanations can be manipulated and geometry is to blame
AK Dombrowski, M Alber, C Anders, M Ackermann, KR Müller, P Kessel
Advances in Neural Information Processing Systems, 13589-13600, 2019
472019
Patternnet and patternlrp–improving the interpretability of neural networks
PJ Kindermans, KT Schütt, M Alber, KR Müller, S Dähne
arXiv preprint arXiv:1705.05598 3, 2017
352017
Distributed optimization of multi-class SVMs
M Alber, J Zimmert, U Dogan, M Kloft
PloS one 12 (6), e0178161, 2017
82017
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
82017
Backprop evolution
M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le
arXiv preprint arXiv:1808.02822, 2018
72018
Learning how to explain neural networks: PatternNet and PatternAttribution.(2018)
PJ Kindermans, KT Schutt, M Alber, KR Muller, D Erhan, B Kim, S Dahne
arXiv preprint arXiv:1705.05598, 2018
62018
Software and application patterns for explanation methods
M Alber
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 399-433, 2019
42019
Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images
P Seegerer, A Binder, R Saitenmacher, M Bockmayr, M Alber, ...
Artificial Intelligence and Machine Learning for Digital Pathology, 16-37, 2020
12020
How to iNNvestigate neural networks' predictions!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
12018
Balancing the composition of word embeddings across heterogenous data sets
S Brandl, D Lassner, M Alber
arXiv preprint arXiv:2001.04693, 2020
2020
Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images
M Bockmayr, M Alber, P Jurmeister, F Klauschen, KR Müller
Artificial Intelligence and Machine Learning for Digital Pathology: State-of …, 2020
2020
Efficient learning machines: from kernel methods to deep learning
M Alber
2019
Masterarbeit: Big Data and Machine Learning: A Case Study with Bump Boost
M Alber
2015
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Articles 1–17