Energy Cell Simulation for Sector Coupling with Power-to-Methane: A Case Study in Lower Bavaria von Bauer, Robert, Schopf, Dominik, Klaus, Grégoire, Brotsack, Raimund, Valdes, Javier

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A framework for regional smart energy planning using volunteered geographic information von Valdes, Javier, Wöllmann, Sebastian, Weber, Andreas, Klaus, Grégoire, Sigl, Christina, Prem, Matthias, Bauer, Robert, Zink, Roland

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

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

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Towards Explainable Artificial Intelligence von Samek, Wojciech, Müller, 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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Layer-Wise Relevance Propagation: An Overview von Montavon, Grégoire, Binder, Alexander, Lapuschkin, Sebastian, Samek, Wojciech, Müller, Klaus-Robert

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

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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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From Clustering to Cluster Explanations via Neural Networks von Kauffmann, Jacob, Esders, Malte, Ruff, Lukas, Montavon, Gregoire, Samek, Wojciech, Muller, Klaus-Robert

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The (Un)reliability of Saliency Methods von Kindermans, Pieter-Jan, Hooker, Sara, Adebayo, Julius, Alber, Maximilian, Schütt, Kristof T., Dähne, Sven, Erhan, Dumitru, Kim, Been

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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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Practical Recommendations for Gradient-Based Training of Deep Architectures von Bengio, Yoshua

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Understanding Patch-Based Learning of Video Data by Explaining Predictions von Anders, Christopher J., Montavon, Grégoire, Samek, Wojciech, Müller, Klaus-Robert

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