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Maximilian Alber
Maximilian Alber
Verified email at tu-berlin.de - Homepage
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Cited by
Year
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
4032019
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
3132017
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
J. Mach. Learn. Res. 20 (93), 1-8, 2019
2632019
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 32, 2019
1672019
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
442017
Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling
A Stenzinger, M Alber, M Allgäuer, P Jurmeister, M Bockmayr, J Budczies, ...
Seminars in cancer biology, 2021
202021
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 30, 2017
132017
Distributed optimization of multi-class SVMs
M Alber, J Zimmert, U Dogan, M Kloft
PloS one 12 (6), e0178161, 2017
122017
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne
Springer, 2019
112019
Backprop evolution
M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le
arXiv preprint arXiv:1808.02822, 2018
112018
Learning how to explain neural networks: Patternnet and patternattribution (2017)
PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne
arXiv preprint arXiv:1705.05598, 2018
112018
Software and application patterns for explanation methods
M Alber
Explainable AI: interpreting, explaining and visualizing deep learning, 399-433, 2019
92019
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
82020
How to iNNvestigate neural networks' predictions!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
42018
Balancing the composition of word embeddings across heterogenous data sets
S Brandl, D Lassner, M Alber
arXiv preprint arXiv:2001.04693, 2020
22020
Efficient learning machines: From kernel methods to deep learning
M Alber
12019
Masterarbeit: Big Data and Machine Learning: A Case Study with Bump Boost
M Alber
12015
Efficient learning machines
M Alber
2019
Explanations can be manipulated and geometry is to blame Open Website
AK Dombrowski, M Alber, CJ Anders, M Ackermann, KR Muller, P Kessel
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Articles 1–19