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pro vyhledávání: '"Couellan, Nicolas"'
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
It is well established that to ensure or certify the robustness of a neural network, its Lipschitz constant plays a prominent role. However, its calculation is NP-hard. In this note, by taking into account activation regions at each layer as new cons
Externí odkaz:
http://arxiv.org/abs/2402.01199
Convolutional Neural Network (CNN) have been widely used in image classification. Over the years, they have also benefited from various enhancements and they are now considered as state of the art techniques for image like data. However, when they ar
Externí odkaz:
http://arxiv.org/abs/2206.01473
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
Publikováno v:
In Transportation Research Part C September 2024 166
Autor:
Sbihi, Mohammed, Couellan, Nicolas
We address the issue of binary classification in Banach spaces in presence of uncertainty. We show that a number of results from classical support vector machines theory can be appropriately generalised to their robust counterpart in Banach spaces. T
Externí odkaz:
http://arxiv.org/abs/2202.08567
Publikováno v:
In Expert Systems With Applications 1 December 2024 255 Part C
Within an industrial manufacturing process, tolerancing is a key player. The dimensions uncertainties management starts during the design phase, with an assessment on variability of parts not yet produced. For one assembly step, we can gain knowledge
Externí odkaz:
http://arxiv.org/abs/1912.09365
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Global Navigation Satellite System (GNSS) signals are subject to different kinds of events causing significant errors in positioning. This work explores the application of Machine Learning (ML) methods of anomaly detection applied to GNSS receiver si
Externí odkaz:
http://arxiv.org/abs/1911.02347