4 papers · 1 filter
Quantization vs Pruning: Insights from the Strong Lottery Ticket Hypothesis
Aakash Kumar, Emanuele Natale
Quantization is an essential technique for making neural networks more efficient, yet our theoretical understanding of it remains limited. Previous works demonstrated that extremel…
Threshold-Driven Streaming Graph: Expansion and Rumor Spreading
Flora Angileri, Andrea Clementi, Emanuele Natale +2
A randomized distributed algorithm called RAES was introduced in [Becchetti et al., SODA 2020] to extract a bounded-degree expander from a dense -vertex expander graph $G = (V,…
On the -majority dynamics with many opinions
Francesco d'Amore, Niccolò D'Archivio, George Giakkoupis +1
We present the first upper bound on the convergence time to consensus of the well-known -majority dynamics with opinions, in the synchronous setting, for and that ar…
Trading-off Accuracy and Communication Cost in Federated Learning
Mattia Jacopo Villani, Emanuele Natale, Frederik Mallmann-Trenn
Leveraging the training-by-pruning paradigm introduced by Zhou et al. and Isik et al. introduced a federated learning protocol that achieves a 34-fold reduction in communication co…