Lipschitz-based robustness estimation for hyperdimensional learning

With the adoption of machine learning models in various practical domains, there is a growing need for evaluating and increasing model robustness. Hyperdimensional computing (HDC) is a neurosymbolic computational paradigm that represents symbols as high dimensional vectors and symbolic operations as...

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主要な著者:	Calvin Yeung, Hamza Errahmouni Barkam, Zhuowen Zou, Sanggeon Yun, Nathaniel D. Bastian, Mohsen Imani
フォーマット:	Artigo
言語:	Inglês
出版事項:	Frontiers Media S.A. 2025-09-01
シリーズ:	Frontiers in Artificial Intelligence
主題:	hyperdimensional computing vector symbolic architectures robustness adversarial attacks classification
オンライン･アクセス:	https://www.frontiersin.org/articles/10.3389/frai.2025.1637105/full
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