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

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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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Methods for interpreting and understanding deep neural networks von Montavon, Grégoire, Samek, Wojciech, Müller, 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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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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Inverse design of 3d molecular structures with conditional generative neural networks von Gebauer, Niklas W. A., Gastegger, Michael, Hessmann, Stefaan S. P., Müller, Klaus-Robert, Schütt, Kristof T.

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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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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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sGDML: Constructing accurate and data efficient molecular force fields using machine learning von Chmiela, Stefan, Sauceda, Huziel E., Poltavsky, Igor, Müller, Klaus-Robert, Tkatchenko, Alexandre

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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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"What is relevant in a text document?": An interpretable machine learning approach von Arras, Leila, Horn, Franziska, Montavon, Grégoire, Müller, Klaus-Robert, Samek, Wojciech

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Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies von Hansen, Katja, Montavon, Grégoire, Biegler, Franziska, Fazli, Siamac, Rupp, Matthias, Scheffler, Matthias, von Lilienfeld, O. Anatole, Tkatchenko, Alexandre, Müller, Klaus-Robert

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Single-trial analysis and classification of ERP components — A tutorial von Blankertz, Benjamin, Lemm, Steven, Treder, Matthias, Haufe, Stefan, Müller, Klaus-Robert

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A convolutional neural network for steady state visual evoked potential classification under ambulatory environment von Kwak, No-Sang, Müller, Klaus-Robert, Lee, Seong-Whan

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Explaining the unique nature of individual gait patterns with deep learning von Horst, Fabian, Lapuschkin, Sebastian, Samek, Wojciech, Müller, Klaus-Robert, Schöllhorn, Wolfgang I.

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Machine learning of solvent effects on molecular spectra and reactions von Gastegger, Michael, Schütt, Kristof T, Müller, Klaus-Robert

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