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pro vyhledávání: '"Akbarnejad, Amir"'
Despite the advances in machine learning and digital pathology, it is not yet clear if machine learning methods can accurately predict molecular information merely from histomorphology. In a quest to answer this question, we built a large-scale datas
Externí odkaz:
http://arxiv.org/abs/2308.01982
The analogy between Gaussian processes (GPs) and deep artificial neural networks (ANNs) has received a lot of interest, and has shown promise to unbox the blackbox of deep ANNs. Existing theoretical works put strict assumptions on the ANN (e.g. requi
Externí odkaz:
http://arxiv.org/abs/2112.09820
Publikováno v:
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, pp. 2847-2856
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be use
Externí odkaz:
http://arxiv.org/abs/1805.07984
Akademický článek
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Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Jun2020, Vol. 14 Issue 5, p1-31, 31p
Autor:
Pin-Yu Chen, Cho-Jui Hsieh
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri?cation. Sections cover adversarial attack, veri?cation and defense, mainly focusing on