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Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions.
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S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, W. Samek, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PLOS ONE 10 (7) (2015) e0130140
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V. Panayotov, G. Chen, D. Povey, S. Khudanpur, Librispeech: an asr corpus based on public domain audio books, in: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, IEEE, 2015, pp. 5206–5210
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I. Sturm, S. Lapuschkin, W. Samek, K.-R. Müller, Interpretable deep neural networks for single-trial eeg classification, Journal of Neuroscience Methods 274 (2016) 141–145
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S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, W. Samek, The layer-wise relevance propagation toolbox for artificial neural networks, Journal of Machine Learning Research 17 (114) (2016) 1–5
2016
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M. T. Ribeiro, S. Singh, C. Guestrin, "why should i trust you?": Explaining the predictions of any classifier, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, Association for Computing Machinery, New York, NY, USA, 2016, p. 1135–1144
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R. C. Fong, A. Vedaldi, Interpretable explanations of black boxes by meaningful perturbation, in: 2017 IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, pp. 3449–3457
2017
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G. Montavon, S. Bach, A. Binder, W. Samek, K.-R. Müller, Explaining nonlinear classification decisions with deep taylor decomposition, Pattern Recognition 65 (2017) 211–222
2017
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M. Kohlbrenner, A. Bauer, S. Nakajima, A. Binder, W. Samek, S. Lapuschkin, Towards best practice in explaining neural network decisions with lrp, in: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp. 1–7
2020
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J. Jeyakumar, J. Noor, Y.-H. Cheng, L. Garcia, M. B. Srivastava, How can i explain this to you? an empirical study of deep neural network explanation methods, in: Neural Information Processing Systems, 2020
2020
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V. Haunschmid, E. Manilow, G. Widmer, audiolime: Listenable explanations using source separation (2020)
2020
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W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, K.-R. Müller, Explaining deep neural networks and beyond: A review of methods and applications, Proceedings of the IEEE 109 (3) (2021) 247–278
2021
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L. Arras, G. Montavon, K.-R. Müller, W. Samek, Explaining recurrent neural network predictions in sentiment analysis, in: EMNLP’17 Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA), 2017, pp. 159–168
2017
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K. T. Schütt, F. Arbabzadah, S. Chmiela, K.-R. Müller, A. Tkatchenko, Quantum-chemical insights from deep tensor neural networks, Nature communications 8 (2017) 13890
2017
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W. Dai, C. Dai, S. Qu, J. Li, S. Das, Very deep convolutional neural networks for raw waveforms, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5-9, 2017, 2017, pp. 421–425
2017
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S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. J. Weiss, K. W. Wilson, CNN architectures for large-scale audio classification, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5-9, 2017, 2017, pp. 131–135
2017
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W. Samek, A. Binder, G. Montavon, S. Lapuschkin, K.-R. Müller, Evaluating the visualization of what a deep neural network has learned, IEEE Transactions on Neural Networks and Learning Systems 28 (11) (2017) 2660–2673
2017
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M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K.-R. Müller, S. Dähne, P.-J. Kindermans, innvestigate neural networks!, J. Mach. Learn. Res. 20 (2018) 93:1–93:8
2018
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W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, K.-R. Müller (Eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Vol. 11700 of Lecture Notes in Computer Science, Springer, Cham, Switzerland, 2019
2019
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S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, K.-R. Müller, Unmasking clever hans predictors and assessing what machines really learn, Nature communications 10 (1) (2019) 1096
2019
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N. Strodthoff, P. Wagner, T. Schaeffter, W. Samek, Deep learning for ecg analysis: Benchmarks and insights from ptb-xl, IEEE Journal of Biomedical and Health Informatics 25 (5) (2021) 1519–1528
2021
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B. W. Schuller, T. Virtanen, M. Riveiro, G. Rizos, J. Han, A. Mesaros, K. Drossos, Towards sonification in multimodal and user-friendlyexplainable artificial intelligence, in: Proceedings of the 2021 International Conference on Multimodal Interaction, ICMI ’21, Association for Computing Machinery, New York, NY, USA, 2021, p. 788–792
2021
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A. B. Melchiorre, V. Haunschmid, M. Schedl, G. Widmer, Lemons: Listenable explanations for music recommender systems, in: D. Hiemstra, M.-F. Moens, J. Mothe, R. Perego, M. Potthast, F. Sebastiani (Eds.), Advances in Information Retrieval, Springer International Publishing, Cham, 2021, pp. 531–536
2021
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A. W. Thomas, C. Ré, R. A. Poldrack, Interpreting mental state decoding with deep learning models, Trends in Cognitive Sciences 26 (11) (2022) 972–986
2022
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D. Slijepcevic, F. Horst, B. Horsak, S. Lapuschkin, A.-M. Raberger, A. Kranzl, W. Samek, C. Breiteneder, W. I. Schöllhorn, M. Zeppelzauer, Explaining machine learning models for clinical gait analysis, ACM Transactions on Computing for Healthcare 3 (2) (2022) 1–27
2022
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A. Wullenweber, A. Akman, B. W. Schuller, Coughlime: Sonified explanations for the predictions of covid-19 cough classifiers, in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022, pp. 1342–1345
2022
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J. Parekh, S. Parekh, P. Mozharovskyi, F. d'Alché-Buc, G. Richard, Listen to interpret: Post-hoc interpretability for audio networks with nmf, in: S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh (Eds.), Advances in Neural Information Processing Systems, Vol. 35, Curran Associates, Inc., 2022, pp. 35270–35283
2022
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R. Achtibat, M. Dreyer, I. Eisenbraun, S. Bosse, T. Wiegand, W. Samek, S. Lapuschkin, From attribution maps to human-understandable explanations through concept relevance propagation, Nature Machine Intelligence 5 (9) (2023) 1006–1019
2023
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F. Klauschen, J. Dippel, P. Keyl, P. Jurmeister, M. Bockmayr, A. Mock, O. Buchstab, M. Alber, L. Ruff, G. Montavon, K.-R. Müller, Toward explainable artificial intelligence for precision pathology, Annual Review of Pathology: Mechanisms of Disease 19 (1) (2024) null
2024
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