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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)Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations., , , , , and . CVPR, page 16143-16152. IEEE, (2023)Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)., , , and . CVPR Workshops, page 3105-3113. Computer Vision Foundation / IEEE, (2020)Pruning by explaining: A novel criterion for deep neural network pruning., , , , , , and . Pattern Recognit., (2021)Reward-Based 1-bit Compressed Federated Distillation on Blockchain., , , , , and . CoRR, (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)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)Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network., , , , and . Digit. Signal Process., (2019)