Zobrazeno 1 - 10
of 7 608
pro vyhledávání: '"P. Ciccone"'
Autor:
Emily J. Ciccone, Lydia Kabugho, Emmanuel Baguma, Rabbison Muhindo, Jonathan J. Juliano, Edgar Mulogo, Ross M. Boyce
Publikováno v:
Microbiology Spectrum, Vol 10, Iss 1 (2022)
Externí odkaz:
https://doaj.org/article/fe4cbf9f23174715bb6bd6c102111d06
Akademický článek
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Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minima, negatively impacting generalization and robustness. Recent approach
Externí odkaz:
http://arxiv.org/abs/2412.03752
We study Yang-Mills theory on four dimensional Anti-de Sitter space. The Dirichlet boundary condition cannot exist at arbitrarily large radius because it would give rise to colored asymptotic states in flat space. As observed in [1] this implies a de
Externí odkaz:
http://arxiv.org/abs/2407.06268
Autor:
Bakas, Odysseas, Ciccone, Valentina, Di Plinio, Francesco, Fraccaroli, Marco, Parissis, Ioannis, Vitturi, Marco
Given an Orlicz space $ L^2 \subseteq X \subseteq L^1$ on $[0,1]$, with submultiplicative Young function ${\mathrm{Y}_X}$, we fully characterize the closed null sets $\Xi$ of the real line with the property that H\"ormander-Mihlin or Marcinkiewicz mu
Externí odkaz:
http://arxiv.org/abs/2406.17521
Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, particularly pronounced in the final classification layer, negatively imp
Externí odkaz:
http://arxiv.org/abs/2406.01116
Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm tha
Externí odkaz:
http://arxiv.org/abs/2406.01820
Akademický článek
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Autor:
Cavagnero, Niccolò, Rosi, Gabriele, Cuttano, Claudia, Pistilli, Francesca, Ciccone, Marco, Averta, Giuseppe, Cermelli, Fabio
Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility, they obtain outstanding performance in multiple segmentation tasks, such as semantic and panoptic, under a single un
Externí odkaz:
http://arxiv.org/abs/2402.19422
We consider Marcinkiewicz multipliers of any lacunary order defined by means of uniformly bounded variation on each lacunary Littlewood--Paley interval of some fixed order $\tau\geq 1$. We prove the optimal endpoint bounds for such multipliers as a c
Externí odkaz:
http://arxiv.org/abs/2401.06083