Zobrazeno 1 - 10
of 3 859
pro vyhledávání: '"Malandrino, A."'
Autor:
Singhal, Chetna, Wu, Yashuo, Malandrino, Francesco, Contreras, Sharon Ladron de Guevara, Levorato, Marco, Chiasserini, Carla Fabiana
Publikováno v:
IEEE SECON 2024
Mobile systems will have to support multiple AI-based applications, each leveraging heterogeneous data sources through DNN architectures collaboratively executed within the network. To minimize the cost of the AI inference task subject to requirement
Externí odkaz:
http://arxiv.org/abs/2410.16723
Pruning neural networks, i.e., removing some of their parameters whilst retaining their accuracy, is one of the main ways to reduce the latency of a machine learning pipeline, especially in resource- and/or bandwidth-constrained scenarios. In this co
Externí odkaz:
http://arxiv.org/abs/2405.13088
Autor:
Singhal, Chetna, Wu, Yashuo, Malandrino, Francesco, Levorato, Marco, Chiasserini, Carla Fabiana
Publikováno v:
IEEE INFOCOM 2024
The increasing pervasiveness of intelligent mobile applications requires to exploit the full range of resources offered by the mobile-edge-cloud network for the execution of inference tasks. However, due to the heterogeneity of such multi-tiered netw
Externí odkaz:
http://arxiv.org/abs/2404.08060
Publikováno v:
Computer Networks, 2024
Intelligent reflecting surfaces (IRSs) have several prominent advantages, including improving the level of wireless communication security and privacy. In this work, we focus on the latter aspect and introduce a strategy to counteract the presence of
Externí odkaz:
http://arxiv.org/abs/2402.14737
Publikováno v:
IEEE WoWMoM 2024
The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value. This leads to a poor dependability}of the resulting ML
Externí odkaz:
http://arxiv.org/abs/2402.14346
Autor:
Chiaramello, Emma, Chiasserini, Carla Fabiana, Malandrino, Francesco, Nordio, Alessandro, Parazzini, Marta, Valcarce, Alvaro
In next-generation networks, cells will be replaced by a collection of points-of-access (PoAs), with overlapping coverage areas and/or different technologies. Along with a promise for greater performance and flexibility, this creates further pressure
Externí odkaz:
http://arxiv.org/abs/2402.14344
The virtual reality (VR) and human-computer interaction (HCI) combination has radically changed the way users approach a virtual environment, increasing the feeling of VR immersion, and improving the user experience and usability. The evolution of th
Externí odkaz:
http://arxiv.org/abs/2302.05660
Publikováno v:
ACM ICMLC 2023
Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself. In this work, we perform a set of experiments to assess to which extent, and in which way, s
Externí odkaz:
http://arxiv.org/abs/2212.02155
Autor:
Malandrino, Francesco, Di Giacomo, Giuseppe, Karamzade, Armin, Levorato, Marco, Chiasserini, Carla Fabiana
Publikováno v:
IEEE INFOCOM 2023
To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance. However, how much and whe
Externí odkaz:
http://arxiv.org/abs/2212.02304
Autor:
Federico Raimondi, Stefano Centanni, Fabrizio Luppi, Stefano Aliberti, Francesco Blasi, Paola Rogliani, Claudio Micheletto, Marco Contoli, Alessandro Sanduzzi Zamparelli, Marialuisa Bocchino, Paolo Busatto, Luca Novelli, Simone Pappacena, Luca Malandrino, Giorgio Lorini, Greta Cairoli, Fabiano Di Marco
Publikováno v:
Monaldi Archives for Chest Disease (2024)
Predictors of outcomes are essential to identifying severe COVID-19 cases and optimizing treatment and care settings. The respiratory rate-oxygenation (ROX) index, originally introduced for predicting the failure of non-invasive support in acute hypo
Externí odkaz:
https://doaj.org/article/a3f2f0ea24bf47399a0d8dd4187a4b12
