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Explaining deep neural networks and beyond: A review of methods and applications

, , , , and . Proceedings of the IEEE, 109 (3): 247--278 (2021)

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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond., , , , , , , and . J. Mach. Learn. Res., (2023)Pruning by explaining: A novel criterion for deep neural network pruning., , , , , , and . Pattern Recognit., (2021)Generative Fractional Diffusion Models., , , , , , , , , and 1 other author(s). CoRR, (2023)Explain to Question not to Justify., and . CoRR, (2024)XAI-based Comparison of Input Representations for Audio Event Classification., , , , and . CoRR, (2023)Machine Learning for Health: Algorithm Auditing & Quality Control., , , , , , , , , and 29 other author(s). J. Medical Syst., 45 (12): 105 (2021)Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence., , , , , , , , and . Inf. Fusion, (2022)Explain and improve: LRP-inference fine-tuning for image captioning models., , , and . Inf. Fusion, (2022)FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning., , , and . IEEE Trans. Neural Networks Learn. Syst., 34 (9): 5531-5543 (September 2023)Evaluating Recurrent Neural Network Explanations, , , and . Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, page 113--126. Florence, Italy, Association for Computational Linguistics, (August 2019)