Zobrazeno 1 - 10
of 6 785
pro vyhledávání: '"A. Malick"'
Autor:
Sharif, M., Sallah, and Malick
Publikováno v:
Chinese J. Phys. 92(2024)794-808
In this study, we utilize the minimal geometric deformation technique of gravitational decoupling to extend the regular Bardeen black hole, leading to the derivation of new black hole solutions within the framework of Rastall theory. By decoupling th
Externí odkaz:
http://arxiv.org/abs/2412.01158
Qubit performance is often reported in terms of a variety of single-value metrics, each providing a facet of the underlying noise mechanism limiting performance. However, the value of these metrics may drift over long time-scales, and reporting a sin
Externí odkaz:
http://arxiv.org/abs/2411.18715
We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using optimal transport distances. For ease of use, it features both scikit-learn compatible estimators for
Externí odkaz:
http://arxiv.org/abs/2410.21231
In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has not yet been applied to Dataset Retrieval. By leveraging metadata and
Externí odkaz:
http://arxiv.org/abs/2407.15546
Publikováno v:
Phys. Rev. B 110, 165149 (2024)
We present the results of a thorough investigation of the physical properties of EuAg$_4$Sb$_2$ single crystals using magnetization, heat capacity, and electrical resistivity measurements. High-quality single crystals, which crystallize in a trigonal
Externí odkaz:
http://arxiv.org/abs/2406.18252
In this paper, we examine the long-run distribution of stochastic gradient descent (SGD) in general, non-convex problems. Specifically, we seek to understand which regions of the problem's state space are more likely to be visited by SGD, and by how
Externí odkaz:
http://arxiv.org/abs/2406.09241
Publikováno v:
Optimization Methods and Software, 2024, pp.1-17. \&\#x27E8;10.1080/10556788.2023.2278089\&\#x27E9
Many problems in machine learning write as the minimization of a sum of individual loss functions over the training examples. These functions are usually differentiable but, in some cases, their gradients are not Lipschitz continuous, which compromis
Externí odkaz:
http://arxiv.org/abs/2404.17190
Autor:
Le, Tam, Malick, Jérôme
Distributionally robust optimization has emerged as an attractive way to train robust machine learning models, capturing data uncertainty and distribution shifts. Recent statistical analyses have proved that robust models built from Wasserstein ambig
Externí odkaz:
http://arxiv.org/abs/2402.11981
Autor:
Sarbanding Sane, Sophie Deli Tene, Abou Abdallah Malick Diouara, Seynabou Coundoul, Malick Mbengue, Yakhya Dieye
Publikováno v:
BMC Microbiology, Vol 24, Iss 1, Pp 1-14 (2024)
Abstract In the context of food transition, the agrifood sector has experienced increasingly strong demand for horticultural products. Fresh fruits and vegetables are mainly vehicles of microorganisms, including pathogens that cause human infections.
Externí odkaz:
https://doaj.org/article/06681d154093416892ec0309560491b0
Publikováno v:
Phys. Rev. B 108, 024428 (2023)
We investigate the physical properties of high-quality single crystals CeGaGe and PrGaGe using magnetization, heat capacity, and magnetotransport measurements. Gallium-indium binary flux was used to grow these single crystals that crystallize in a bo
Externí odkaz:
http://arxiv.org/abs/2401.15907