no code implementations • 22 Feb 2024 • Francesco Malandrino, Giuseppe Di Giacomo, Marco Levorato, Carla Fabiana Chiasserini
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.
no code implementations • 2 Dec 2022 • Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade, Marco Levorato, Carla Fabiana Chiasserini
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.
no code implementations • 23 Feb 2022 • Francesco Malandrino, Carla Fabiana Chiasserini, Giuseppe Di Giacomo
In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available.
1 code implementation • 22 Jan 2021 • Vien Ngoc Dang, Francesco Galati, Rosa Cortese, Giuseppe Di Giacomo, Viola Marconetto, Prateek Mathur, Karim Lekadir, Marco Lorenzi, Ferran Prados, Maria A. Zuluaga
First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image.