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Maximilian Alber
Maximilian Alber
Dirección de correo verificada de tu-berlin.de - Página principal
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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
6612019
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
3932017
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
J. Mach. Learn. Res. 20 (93), 1-8, 2019
3812019
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
3062019
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 84, 129-143, 2022
532022
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
462017
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
182017
Backprop evolution
M Alber, I Bello, B Zoph, PJ Kindermans, P Ramachandran, Q Le
arXiv preprint arXiv:1808.02822, 2018
172018
Distributed optimization of multi-class SVMs
M Alber, J Zimmert, U Dogan, M Kloft
PloS one 12 (6), e0178161, 2017
142017
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: State-of …, 2020
132020
Software and application patterns for explanation methods
M Alber
Explainable AI: interpreting, explaining and visualizing deep learning, 399-433, 2019
132019
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
122018
Toward explainable artificial intelligence for precision pathology
F Klauschen, J Dippel, P Keyl, P Jurmeister, M Bockmayr, A Mock, ...
Annual Review of Pathology: Mechanisms of Disease 19, 541-570, 2024
62024
Deep learning assisted diagnosis of onychomycosis on whole-slide images
P Jansen, A Creosteanu, V Matyas, A Dilling, A Pina, A Saggini, ...
Journal of Fungi 8 (9), 912, 2022
62022
Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights
L Schweizer, P Seegerer, H Kim, R Saitenmacher, A Muench, L Barnick, ...
Neuropathology and Applied Neurobiology 49 (1), e12866, 2023
42023
How to iNNvestigate neural networks' predictions!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
42018
Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law
D Kim, M Alber, MW Kwok, J MitroviĆ, C Ramirez-Atencia, JÚARÍ PÉrez, ...
GRUR International 71 (4), 295-321, 2022
32022
Ten Assumptions About Artificial Intelligence That Can Mislead Patent Law Analysis
D Kim, M Alber, MW Kwok, J Mitrovic, C Ramirez-Atencia, ...
Max Planck Institute for Innovation & Competition Research Paper, 2021
32021
68MO Generalization of a deep learning model for HER2 status predictions on H&E-stained whole slide images derived from 3 neoadjuvant clinical studies
M Hägele, KR Müller, C Denkert, A Schneeweiss, BV Sinn, M Untch, ...
Annals of Oncology 33, S572-S573, 2022
22022
Balancing the composition of word embeddings across heterogenous data sets
S Brandl, D Lassner, M Alber
arXiv preprint arXiv:2001.04693, 2020
12020
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