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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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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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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"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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Machine learning of molecular electronic properties in chemical compound space von Montavon, Grégoire, Rupp, Matthias, Gobre, Vivekanand, Vazquez-Mayagoitia, Alvaro, Hansen, Katja, Tkatchenko, Alexandre, Müller, Klaus-Robert, Anatole von Lilienfeld, O

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Machine-learning Inference of the Interior Structure of Low-mass Exoplanets von Baumeister, Philipp, Padovan, Sebastiano, Tosi, Nicola, Montavon, Grégoire, Nettelmann, Nadine, MacKenzie, Jasmine, Godolt, Mareike

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Analyzing Atomic Interactions in Molecules as Learned by Neural Networks von Esders, Malte, Schnake, Thomas, Lederer, Jonas, Kabylda, Adil, Montavon, Grégoire, Tkatchenko, Alexandre, Müller, Klaus-Robert

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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning von Samek, Wojciech, Montavon, Grégoire, Vedaldi, Andrea, Hansen, Lars Kai, 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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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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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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Patient-level proteomic network prediction by explainable artificial intelligence von Keyl, Philipp, Bockmayr, Michael, Heim, Daniel, Dernbach, Gabriel, Montavon, Grégoire, Müller, Klaus-Robert, Klauschen, Frederick

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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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GraphKKE: graph Kernel Koopman embedding for human microbiome analysis von Melnyk, Kateryna, Klus, Stefan, Montavon, Grégoire, Conrad, Tim O. F.

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Explainable AI for time series via Virtual Inspection Layers von Vielhaben, Johanna, Lapuschkin, Sebastian, Montavon, Grégoire, Samek, Wojciech

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Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison von Montavon, Grégoire

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Explaining predictive uncertainty by exposing second-order effects von Bley, Florian, Lapuschkin, Sebastian, Samek, Wojciech, Montavon, Grégoire

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Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers von Heim, Daniel, Montavon, Grégoire, Hufnagl, Peter, Müller, Klaus-Robert, Klauschen, Frederick

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Towards explaining anomalies: A deep Taylor decomposition of one-class models von Kauffmann, Jacob, Müller, Klaus-Robert, Montavon, Grégoire

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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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