Maximilian Alber

According to our database, Maximilian Alber authored at least 17 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics.

Jonas Dippel, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, Maximilian Alber, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen

CoRR, 2024

RudolfV: A Foundation Model by Pathologists for Pathologists.

Jonas Dippel, Barbara Feulner, Tobias Winterhoff, Simon Schallenberg, Gabriel Dernbach, Andreas Kunft, Stephan Tietz, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen, Maximilian Alber

CoRR, 2024

2023

Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides.

Miriam Hägele, Johannes Eschrich, Lukas Ruff, Maximilian Alber, Simon Schallenberg, Adrien Guillot, Christoph Roderburg, Frank Tacke, Frederick Klauschen

CoRR, 2023

2020

Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images.

Philipp Seegerer, Alexander Binder, René Saitenmacher, Michael Bockmayr, Maximilian Alber, Philipp Jurmeister, Frederick Klauschen, Klaus-Robert Müller

AI and ML for Digital Pathology, 2020

Balancing the composition of word embeddings across heterogenous data sets.

Stephanie Brandl, David Lassner, Maximilian Alber

CoRR, 2020

2019

The (Un)reliability of Saliency Methods.

Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim

Proceedings of the Explainable AI: Interpreting, 2019

Software and Application Patterns for Explanation Methods.

Maximilian Alber

Proceedings of the Explainable AI: Interpreting, 2019

Efficient learning machines: from kernel methods to deep learning.

Maximilian Alber

PhD thesis, 2019

iNNvestigate Neural Networks!

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans

J. Mach. Learn. Res., 2019

Software and application patterns for explanation methods.

Maximilian Alber

CoRR, 2019

Explanations can be manipulated and geometry is to blame.

Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Backprop Evolution.

Maximilian Alber, Irwan Bello, Barret Zoph, Pieter-Jan Kindermans, Prajit Ramachandran, Quoc V. Le

CoRR, 2018

Learning how to explain neural networks: PatternNet and PatternAttribution.

Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

The (Un)reliability of saliency methods.

Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim

CoRR, 2017

PatternNet and PatternLRP - Improving the interpretability of neural networks.

Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Sven Dähne

CoRR, 2017

An Empirical Study on The Properties of Random Bases for Kernel Methods.

Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Distributed Optimization of Multi-Class SVMs.

Maximilian Alber, Julian Zimmert, Ürün Dogan, Marius Kloft

CoRR, 2016