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Evaluating Recurrent Neural Network Explanations, , , и . Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, стр. 113--126. Florence, Italy, Association for Computational Linguistics, (августа 2019)Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations., , , , , и . CVPR, стр. 16143-16152. IEEE, (2023)Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)., , , и . CVPR Workshops, стр. 3105-3113. Computer Vision Foundation / IEEE, (2020)Pruning by explaining: A novel criterion for deep neural network pruning., , , , , , и . Pattern Recognit., (2021)Reward-Based 1-bit Compressed Federated Distillation on Blockchain., , , , , и . CoRR, (2021)Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence., , , , , , , , и . Inf. Fusion, (2022)Explain and improve: LRP-inference fine-tuning for image captioning models., , , и . Inf. Fusion, (2022)Explainable AI Methods - A Brief Overview., , , , и . xxAI@ICML, том 13200 из Lecture Notes in Computer Science, стр. 13-38. Springer, (2020)FedAUXfdp: Differentially Private One-Shot Federated Distillation., , , и . FL@IJCAI, том 13448 из Lecture Notes in Computer Science, стр. 100-114. Springer, (2022)Estimation of distortion sensitivity for visual quality prediction using a convolutional neural network., , , , и . Digit. Signal Process., (2019)