Zobrazeno 1 - 10
of 500
pro vyhledávání: '"Bacciu P"'
Autor:
Betello, Filippo, Purificato, Antonio, Siciliano, Federico, Trappolini, Giovanni, Bacciu, Andrea, Tonellotto, Nicola, Silvestri, Fabrizio
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in user behaviour, significantly improving recommendation accuracy and rel
Externí odkaz:
http://arxiv.org/abs/2408.03873
Decentraland is a blockchain-based social virtual world touted to be a creative space owned by its community. In it, users can publish wearables used to customize avatars, which can be then sold or given away via blockchain transfers. Decentral Games
Externí odkaz:
http://arxiv.org/abs/2407.15625
The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate halluc
Externí odkaz:
http://arxiv.org/abs/2406.19537
Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the phenomenon of catastrophic forgetting and the high demand of computati
Externí odkaz:
http://arxiv.org/abs/2406.09835
Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling spatio-temporal information on streams of irregularly sampled events. While many methods have been proposed recently, we find that most message passing-, recurrent- or self
Externí odkaz:
http://arxiv.org/abs/2406.02740
The need for modelling causal knowledge at different levels of granularity arises in several settings. Causal Abstraction provides a framework for formalizing this problem by relating two Structural Causal Models at different levels of detail. Despit
Externí odkaz:
http://arxiv.org/abs/2406.00394
Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require a large amount of data to produce good recommendations, such as a lar
Externí odkaz:
http://arxiv.org/abs/2405.19749
The dynamics of information diffusion within graphs is a critical open issue that heavily influences graph representation learning, especially when considering long-range propagation. This calls for principled approaches that control and regulate the
Externí odkaz:
http://arxiv.org/abs/2405.17163
Autor:
Gravina, Alessio, Eliasof, Moshe, Gallicchio, Claudio, Bacciu, Davide, Schönlieb, Carola-Bibiane
A common problem in Message-Passing Neural Networks is oversquashing -- the limited ability to facilitate effective information flow between distant nodes. Oversquashing is attributed to the exponential decay in information transmission as node dista
Externí odkaz:
http://arxiv.org/abs/2405.01009
Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sp
Externí odkaz:
http://arxiv.org/abs/2404.19508