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Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

, , , , , , , and . (2022)cite arxiv:2202.06861Comment: 4 pages, 1 figure, 1 table.

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Achieving Generalizable Robustness of Deep Neural Networks by Stability Training., , and . GCPR, volume 11824 of Lecture Notes in Computer Science, page 360-373. Springer, (2019)Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning., , , , , and . CoRR, (2019)Finding and removing Clever Hans: Using explanation methods to debug and improve deep models., , , , , and . Inf. Fusion, (2022)Explaining Machine Learning Models for Clinical Gait Analysis., , , , , , , , , and . ACM Trans. Comput. Heal., 3 (2): 14:1-14:27 (2022)Towards Explainable Artificial Intelligence., and . Explainable AI, volume 11700 of Lecture Notes in Computer Science, Springer, (2019)History Dependent Significance Coding for Incremental Neural Network Compression., , , , , , , , , and . ICIP, page 3541-3545. IEEE, (2022)Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond., , , , and . CoRR, (2020)CFD: Communication-Efficient Federated Distillation via Soft-Label Quantization and Delta Coding., , , and . IEEE Trans. Netw. Sci. Eng., 9 (4): 2025-2038 (2022)ECQ x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs., , , , and . xxAI@ICML, volume 13200 of Lecture Notes in Computer Science, page 271-296. Springer, (2020)Explaining the Predictions of Unsupervised Learning Models., , , and . xxAI@ICML, volume 13200 of Lecture Notes in Computer Science, page 117-138. Springer, (2020)