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Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples., , and . CoRR, (2017)Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks., , and . CoRR, (2017)A constrained $$$$ ℓ 1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models., , and . Mach. Learn., 106 (9-10): 1381-1417 (2017)Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks., , , and . PSB, page 254-265. (2017)Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data., , , , and . ACM Trans. Knowl. Discov. Data, 10 (4): 38:1-38:23 (2016)Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers., , , and . IEEE Symposium on Security and Privacy Workshops, page 50-56. IEEE Computer Society, (2018)Sentiment Classification with Supervised Sequence Embedding., , , and . ECML/PKDD (1), volume 7523 of Lecture Notes in Computer Science, page 159-174. Springer, (2012)Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management., , , and . IEEE Trans. Cloud Comput., 10 (3): 1848-1863 (2022)Towards Improving Adversarial Training of NLP Models., and . EMNLP (Findings), page 945-956. Association for Computational Linguistics, (2021)Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning., and . VizSEC, page 1-4. IEEE Computer Society, (2017)