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Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems., , , , , и . CoRR, (2021)Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data., , , , и . CoRR, (2022)Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding., , и . IJCNN, стр. 1-8. IEEE, (2019)BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants., , , , и . CoRR, (2023)Assessing the reliability of point mutation as data augmentation for deep learning with genomic data., , , , , и . BMC Bioinform., 25 (1): 170 (декабря 2024)How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples., , и . CoRR, (2018)Exact Feature Collisions in Neural Networks., , , , и . CoRR, (2022)Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning., , , , , и . IHCI (2), том 12616 из Lecture Notes in Computer Science, стр. 254-266. Springer, (2020)Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training., , , , , , , и . CoRR, (2023)Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability., , , , , и . CoRR, (2020)