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Plos One
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August 12, 2017
"What is relevant in a text document?": An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, et al.
Genome Medicine
|
November 17, 2018
Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers
Daniel Heim, Grégoire Montavon, Peter Hufnagl, et al.
Neural Networks : the Official Journal of the International Neural Network Society
|
September 3, 2023
Learning domain invariant representations by joint Wasserstein distance minimization
Léo Andéol, Yusei Kawakami, Yuichiro Wada, et al.
Plos One
|
July 11, 2015
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, et al.
Nature Communications
|
March 13, 2019
Unmasking Clever Hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, et al.
Science Advances
|
October 23, 2024
Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables
Oliver Eberle, Jochen Büttner, Hassan El-Hajj, et al.
Journal of Chemical Theory and Computation
|
January 10, 2025
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Malte Esders, Thomas Schnake, Jonas Lederer, et al.
NPJ Precision Oncology
|
June 7, 2022
Patient-level proteomic network prediction by explainable artificial intelligence
Philipp Keyl, Michael Bockmayr, Daniel Heim, et al.
Eclinicalmedicine
|
July 2, 2026
Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models
Lea Fierley, Jakob Versnjak, Grischa Gabel, et al.
Nucleic Acids Research
|
January 11, 2023
Single-cell gene regulatory network prediction by explainable AI
Philipp Keyl, Philip Bischoff, Gabriel Dernbach, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 17) with videos related to
Sort By:
Page
of 2
Plos One
|
August 12, 2017
"What is relevant in a text document?": An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, et al.
Genome Medicine
|
November 17, 2018
Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers
Daniel Heim, Grégoire Montavon, Peter Hufnagl, et al.
Neural Networks : the Official Journal of the International Neural Network Society
|
September 3, 2023
Learning domain invariant representations by joint Wasserstein distance minimization
Léo Andéol, Yusei Kawakami, Yuichiro Wada, et al.
Plos One
|
July 11, 2015
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, et al.
Nature Communications
|
March 13, 2019
Unmasking Clever Hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, et al.
Science Advances
|
October 23, 2024
Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables
Oliver Eberle, Jochen Büttner, Hassan El-Hajj, et al.
Journal of Chemical Theory and Computation
|
January 10, 2025
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Malte Esders, Thomas Schnake, Jonas Lederer, et al.
NPJ Precision Oncology
|
June 7, 2022
Patient-level proteomic network prediction by explainable artificial intelligence
Philipp Keyl, Michael Bockmayr, Daniel Heim, et al.
Eclinicalmedicine
|
July 2, 2026
Prediction of hypertension and restenosis under guideline-directed management in aortic coarctation: development and validation of machine-learning models
Lea Fierley, Jakob Versnjak, Grischa Gabel, et al.
Nucleic Acids Research
|
January 11, 2023
Single-cell gene regulatory network prediction by explainable AI
Philipp Keyl, Philip Bischoff, Gabriel Dernbach, et al.
Page
of 2