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

, , , , , , , and . J. Mach. Learn. Res., (2023)

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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)Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)., , , and . CVPR Workshops, page 3105-3113. Computer Vision Foundation / IEEE, (2020)Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations., , , , , and . CVPR, page 16143-16152. IEEE, (2023)Explainable AI Methods - A Brief Overview., , , , and . xxAI@ICML, volume 13200 of Lecture Notes in Computer Science, page 13-38. Springer, (2020)FedAUXfdp: Differentially Private One-Shot Federated Distillation., , , and . FL@IJCAI, volume 13448 of Lecture Notes in Computer Science, page 100-114. Springer, (2022)DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression., , , , , , , , , and 2 other author(s). CoRR, (2019)Understanding Patch-Based Learning by Explaining Predictions., , , and . CoRR, (2018)iNNvestigate Neural Networks!, , , , , , , , , and . J. Mach. Learn. Res., (2019)