Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints von Sattler, Felix, Muller, Klaus-Robert, Samek, Wojciech

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Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data von Sattler, Felix, Wiedemann, Simon, Muller, Klaus-Robert, Samek, Wojciech

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Machine Learning for Molecular Simulation von Noé, Frank, Tkatchenko, Alexandre, Müller, Klaus-Robert, Clementi, Cecilia

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Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space von Hansen, Katja, Biegler, Franziska, Ramakrishnan, Raghunathan, Pronobis, Wiktor, von Lilienfeld, O. Anatole, Müller, Klaus-Robert, Tkatchenko, Alexandre

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Unmasking Clever Hans predictors and assessing what machines really learn von Lapuschkin, Sebastian, Wäldchen, Stephan, Binder, Alexander, Montavon, Grégoire, Samek, Wojciech, Müller, Klaus-Robert

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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications von Samek, Wojciech, Montavon, Gregoire, Lapuschkin, Sebastian, Anders, Christopher J., Muller, Klaus-Robert

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Methods for interpreting and understanding deep neural networks von Montavon, Grégoire, Samek, Wojciech, Müller, Klaus-Robert

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SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects von Unke, Oliver T., Chmiela, Stefan, Gastegger, Michael, Schütt, Kristof T., Sauceda, Huziel E., Müller, Klaus-Robert

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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems von Keith, John A, Vassilev-Galindo, Valentin, Cheng, Bingqing, Chmiela, Stefan, Gastegger, Michael, Müller, Klaus-Robert, Tkatchenko, Alexandre

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Explaining nonlinear classification decisions with deep Taylor decomposition von Montavon, Grégoire, Lapuschkin, Sebastian, Binder, Alexander, Samek, Wojciech, Müller, Klaus-Robert

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Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment von Bosse, Sebastian, Maniry, Dominique, Muller, Klaus-Robert, Wiegand, Thomas, Samek, Wojciech

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Evaluating the Visualization of What a Deep Neural Network Has Learned von Samek, Wojciech, Binder, Alexander, Montavon, Gregoire, Lapuschkin, Sebastian, Muller, Klaus-Robert

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Towards exact molecular dynamics simulations with machine-learned force fields von Chmiela, Stefan, Sauceda, Huziel E., Müller, Klaus-Robert, Tkatchenko, Alexandre

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Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules von Pronobis, Wiktor, Tkatchenko, Alexandre, Müller, Klaus-Robert

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Interpretable deep neural networks for single-trial EEG classification von Sturm, Irene, Lapuschkin, Sebastian, Samek, Wojciech, Müller, Klaus-Robert

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A Unifying Review of Deep and Shallow Anomaly Detection von Ruff, Lukas, Kauffmann, Jacob R., Vandermeulen, Robert A., Montavon, Gregoire, Samek, Wojciech, Kloft, Marius, Dietterich, Thomas G., Muller, Klaus-Robert

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Machine Learning Force Fields von Unke, Oliver T, Chmiela, Stefan, Sauceda, Huziel E, Gastegger, Michael, Poltavsky, Igor, Schütt, Kristof T, Tkatchenko, Alexandre, Müller, Klaus-Robert

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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation von Bach, Sebastian, Binder, Alexander, Montavon, Grégoire, Klauschen, Frederick, Müller, Klaus-Robert, Samek, Wojciech

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Bypassing the Kohn-Sham equations with machine learning von Brockherde, Felix, Vogt, Leslie, Li, Li, Tuckerman, Mark E., Burke, Kieron, Müller, Klaus-Robert

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Quantum chemical accuracy from density functional approximations via machine learning von Bogojeski, Mihail, Vogt-Maranto, Leslie, Tuckerman, Mark E., Müller, Klaus-Robert, Burke, Kieron

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