activity
20212026
most citedLacking the embedding of a word? Look it up into a traditional dictionary

10 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.HE2026

Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection

Alberto Garinei, Stefano Speziali, Alessandro Vispa +11

We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sky with self-supervised spatio-…

cs.LG2026

Graph Vector Field: A Unified Framework for Multimodal Health Risk Assessment from Heterogeneous Wearable and Environmental Data Streams

Silvano Coletti, Francesca Fallucchi

Digital health research has advanced dynamic graph-based disease models, topological learning on simplicial complexes, and multimodal mixture-of-experts architectures, but these st…

cs.CY2022

Active Informed Consent to Boost the Application of Machine Learning in Medicine

Marco Gerardi, Katarzyna Barud, Marie-Catherine Wagner +5

Machine Learning may push research in precision medicine to unprecedented heights. To succeed, machine learning needs a large amount of data, often including personal data. Therefo…

cs.CL2022★ 6 cited

The Dark Side of the Language: Pre-trained Transformers in the DarkNet

Leonardo Ranaldi, Aria Nourbakhsh, Arianna Patrizi +4

Pre-trained Transformers are challenging human performances in many NLP tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In…

cs.CL2021★ 10 cited

Lacking the embedding of a word? Look it up into a traditional dictionary

Elena Sofia Ruzzetti, Leonardo Ranaldi, Michele Mastromattei +2

Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often…