Zobrazeno 1 - 10
of 4 171
pro vyhledávání: '"A, Scarselli"'
Autor:
Corradini, Barbara Toniella, Shukor, Mustafa, Couairon, Paul, Couairon, Guillaume, Scarselli, Franco, Cord, Matthieu
Foundation models have exhibited unprecedented capabilities in tackling many domains and tasks. Models such as CLIP are currently widely used to bridge cross-modal representations, and text-to-image diffusion models are arguably the leading models in
Externí odkaz:
http://arxiv.org/abs/2403.20105
Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion; based on a message passing mechanism, GNNs have gained increasing popularity due to their intui
Externí odkaz:
http://arxiv.org/abs/2401.12362
Autor:
Bucarelli, Maria Sofia, D'Inverno, Giuseppe Alessio, Bianchini, Monica, Scarselli, Franco, Silvestri, Fabrizio
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent. This search for an appropriate description, both anal
Externí odkaz:
http://arxiv.org/abs/2401.03824
Publikováno v:
Proceedings of The 31st Annual International ACM SIGIR Conference (SIGIR 2008) - Workshop: Learning to Rank for Information Retrieval (LR4IR)
The problem of relevance ranking consists of sorting a set of objects with respect to a given criterion. Since users may prefer different relevance criteria, the ranking algorithms should be adaptable to the user needs. Two main approaches exist in l
Externí odkaz:
http://arxiv.org/abs/2311.01864
Autor:
Longa, Antonio, Lachi, Veronica, Santin, Gabriele, Bianchini, Monica, Lepri, Bruno, Lio, Pietro, Scarselli, Franco, Passerini, Andrea
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many real-world systems are dynamic in nature, since the graph and node/edge attributes change over time. In recent years, GNN-base
Externí odkaz:
http://arxiv.org/abs/2302.01018
Autor:
Beddar-Wiesing, Silvia, D'Inverno, Giuseppe Alessio, Graziani, Caterina, Lachi, Veronica, Moallemy-Oureh, Alice, Scarselli, Franco, Thomas, Josephine Maria
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weis
Externí odkaz:
http://arxiv.org/abs/2210.03990
Autor:
Börner, Georg, Schröder, Malte, Scarselli, Davide, Budanur, Nazmi Burak, Hof, Björn, Timme, Marc
Standard epidemic models exhibit one continuous, second order phase transition to macroscopic outbreaks. However, interventions to control outbreaks may fundamentally alter epidemic dynamics. Here we reveal how such interventions modify the type of p
Externí odkaz:
http://arxiv.org/abs/2207.08879
Autor:
Alessio Antolini, Andrea Lico, Francesco Zavalloni, Eleonora Franchi Scarselli, Antonio Gnudi, Mattia Luigi Torres, Roberto Canegallo, Marco Pasotti
Publikováno v:
IEEE Open Journal of the Solid-State Circuits Society, Vol 4, Pp 69-82 (2024)
This article presents a readout scheme for analog in-memory computing (AIMC) based on an embedded phase-change memory (ePCM). Conductance time drift is overcome with a hardware compensation technique based on a reference cell conductance tracking (RC
Externí odkaz:
https://doaj.org/article/410dac993f694d3ba1f6f8a8600151c0
Autor:
Irene Fasciani, Francesco Petragnano, Ziming Wang, Ruairidh Edwards, Narasimha Telugu, Ilaria Pietrantoni, Ulrike Zabel, Henrik Zauber, Marlies Grieben, Maria E Terzenidou, Jacopo Di Gregorio, Cristina Pellegrini, Silvano Santini, Anna R Taddei, Bärbel Pohl, Stefano Aringhieri, Marco Carli, Gabriella Aloisi, Francesco Marampon, Eve Charlesworth, Alexandra Roman, Sebastian Diecke, Vincenzo Flati, Franco Giorgi, Fernanda Amicarelli, Andrew B Tobin, Marco Scarselli, Kostas Tokatlidis, Mario Rossi, Martin J Lohse, Paolo Annibale, Roberto Maggio
Publikováno v:
PLoS Biology, Vol 22, Iss 4, p e3002582 (2024)
Muscarinic acetylcholine receptors are prototypical G protein-coupled receptors (GPCRs), members of a large family of 7 transmembrane receptors mediating a wide variety of extracellular signals. We show here, in cultured cells and in a murine model,
Externí odkaz:
https://doaj.org/article/55337ca4f8c84555b649a08804c2bd5c
Autor:
Bongini, Pietro, Scarselli, Franco, Bianchini, Monica, Dimitri, Giovanna Maria, Pancino, Niccolò, Liò, Pietro
Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery.
Externí odkaz:
http://arxiv.org/abs/2202.08147