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This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and ...
Apostol Vassilev +3 more
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Apostol Vassilev +3 more
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Adversarial Machine Learning in Wireless Communications Using RF Data: A Review
IEEE Communications Surveys and Tutorials, 2023Damilola Adesina +2 more
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Hierarchical Adversarial Inverse Reinforcement Learning
IEEE Transactions on Neural Networks and Learning SystemsImitation learning (IL) has been proposed to recover the expert policy from demonstrations. However, it would be difficult to learn a single monolithic policy for highly complex long-horizon tasks of which the expert policy usually contains subtask hierarchies.
Jiayu Chen, Tian Lan, Vaneet Aggarwal
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A Survey on Generative Adversarial Networks: Variants, Applications, and Training
ACM Computing Surveys, 2022Songyuan Li
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Generative Adversarial Networks in Time Series: A Systematic Literature Review
ACM Computing Surveys, 2023Eoin Brophy, Zhengwei Wang, Qi She
exaly