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The purpose of this paper is to employ the language of Cartan moving frames to study the geometry of the data manifolds and its Riemannian structure, via the data information metric and its curvature at data points. Using this framework and through e
Externí odkaz:
http://arxiv.org/abs/2409.12057
Deep learning models are known to be vulnerable to adversarial attacks. Adversarial learning is therefore becoming a crucial task. We propose a new vision on neural network robustness using Riemannian geometry and foliation theory. The idea is illust
Externí odkaz:
http://arxiv.org/abs/2203.00922
Akademický článek
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Deep learning models are known to be vulnerable to adversarial attacks. Adversarial learning is therefore becoming a crucial task. We propose a new vision on neural network robustness using Riemannian geometry and foliation theory. The idea is illust
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7671d8656edf5f57dfc76f049d348222
https://hal-enac.archives-ouvertes.fr/hal-03593479v1/file/FIM_foliation_and_neural_networks_attacks.pdf
https://hal-enac.archives-ouvertes.fr/hal-03593479v1/file/FIM_foliation_and_neural_networks_attacks.pdf