Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Grégoire Montavon"'
Autor:
Philipp Keyl, Michael Bockmayr, Daniel Heim, Gabriel Dernbach, Grégoire Montavon, Klaus-Robert Müller, Frederick Klauschen
Publikováno v:
npj Precision Oncology, Vol 6, Iss 1, Pp 1-10 (2022)
Abstract Understanding the pathological properties of dysregulated protein networks in individual patients’ tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling network
Externí odkaz:
https://doaj.org/article/4698d5ecc239499286a84d1e4fc6b461
Publikováno v:
Applied Network Science, Vol 5, Iss 1, Pp 1-22 (2020)
Abstract More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to dir
Externí odkaz:
https://doaj.org/article/29778f44c4ee48e681670db68031585b
Autor:
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-8 (2019)
Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decisio
Externí odkaz:
https://doaj.org/article/ae85cf785d3645e68f9b5bfe27287566
Publikováno v:
Genome Medicine, Vol 10, Iss 1, Pp 1-17 (2018)
Abstract Background Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale b
Externí odkaz:
https://doaj.org/article/8d2148cfe5ab49e4b0856547cda93f84
Publikováno v:
PLoS ONE, Vol 12, Iss 8, p e0181142 (2017)
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate ve
Externí odkaz:
https://doaj.org/article/fa524c569ac84999872df00749ef7ce1
Autor:
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek
Publikováno v:
PLoS ONE, Vol 10, Iss 7, p e0130140 (2015)
Understanding and interpreting classification decisions of automated image classification systems is of high value in many applications, as it allows to verify the reasoning of the system and provides additional information to the human expert. Altho
Externí odkaz:
https://doaj.org/article/1889e731d1be48abb891b7c2d7c4c899
Autor:
Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, O Anatole von Lilienfeld
Publikováno v:
New Journal of Physics, Vol 15, Iss 9, p 095003 (2013)
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure–property relation
Externí odkaz:
https://doaj.org/article/353547643bb84effb82e2e37aed65a10
Autor:
Kristof T. Schütt, Jonas Lederer, Oliver Eberle, Shinichi Nakajima, Klaus-Robert Mueller, Grégoire Montavon, Thomas Schnake
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:7581-7596
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explainable AI approaches are not applicable. To a large extent, GNNs have re
Publikováno v:
Mathematical Aspects of Deep Learning ISBN: 9781009025096
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97934ea560fa9d079b8a5a128334126c
https://doi.org/10.1017/9781009025096.006
https://doi.org/10.1017/9781009025096.006
Autor:
Jacob R. Kauffmann, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Lukas Ruff, Klaus-Robert Müller, Grégoire Montavon, Robert A. Vandermeulen
Publikováno v:
Proceedings of the IEEE. 109:756-795
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection