Explaining Recurrent Neural Network Predictions in Sentiment Analysis L Arras, G Montavon, KR Müller, W Samek EMNLP 2017 Workshop on Computational Approaches to Subjectivity, Sentiment …, 2017 | 284 | 2017 |

"What is relevant in a text document?": An interpretable machine learning approach L Arras, F Horn, G Montavon, KR Müller, W Samek PLOS ONE 12 (8), e0181142, 2017 | 272 | 2017 |

Explaining Predictions of Non-Linear Classifiers in NLP L Arras, F Horn, G Montavon, KR Müller, W Samek ACL 2016 Representation Learning for NLP (Rep4NLP), 1-7, 2016 | 108 | 2016 |

Explaining and Interpreting LSTMs L Arras, J Arjona-Medina, M Widrich, G Montavon, M Gillhofer, KR Müller, ... Springer LNCS, Explainable AI: Interpreting, Explaining and Visualizing Deep …, 2019 | 54 | 2019 |

Evaluating Recurrent Neural Network Explanations L Arras, A Osman, KR Müller, W Samek ACL 2019 BlackboxNLP (oral), Analyzing & Interpreting Neural Networks for …, 2019 | 50 | 2019 |

Towards Ground Truth Evaluation of Visual Explanations A Osman, L Arras, W Samek arXiv:2003.07258v1, 2020 | 18 | 2020 |

Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L Arras, A Osman, W Samek arXiv:2003.07258v2, 2021 | 8 | 2021 |

Causes of Outcome Learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome A Rieckmann, P Dworzynski, L Arras, S Lapuschkin, W Samek, OA Arah, ... medRxiv 2020.12.10.20225243, 2020 | 2 | 2020 |

Explaining the Decisions of Convolutional and Recurrent Neural Networks W Samek, L Arras, A Osman, G Montavon, KR Müller Mathematical Aspects of Deep Learning (to appear), Cambridge University Press, 2021 | | 2021 |