Zobrazeno 1 - 10
of 184
pro vyhledávání: '"Scarselli, Franco"'
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
Publikováno v:
In Neurocomputing 21 January 2025 614
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
Autor:
Suárez-Varela, José, Almasan, Paul, Ferriol-Galmés, Miquel, Rusek, Krzysztof, Geyer, Fabien, Cheng, Xiangle, Shi, Xiang, Xiao, Shihan, Scarselli, Franco, Cabellos-Aparicio, Albert, Barlet-Ros, Pere
Publikováno v:
IEEE Network, 2022
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental compon
Externí odkaz:
http://arxiv.org/abs/2112.14792
Publikováno v:
In Neurocomputing 28 July 2024 591