Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Trappolini, Giovanni"'
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
Retrieval Augmented Generation (RAG) represents a significant advancement in artificial intelligence combining a retrieval phase with a generative phase, with the latter typically being powered by large language models (LLMs). The current common prac
Externí odkaz:
http://arxiv.org/abs/2406.14972
The ability to read, understand and find important information from written text is a critical skill in our daily lives for our independence, comfort and safety. However, a significant part of our society is affected by partial vision impairment, whi
Externí odkaz:
http://arxiv.org/abs/2404.09254
Autor:
Cuconasu, Florin, Trappolini, Giovanni, Siciliano, Federico, Filice, Simone, Campagnano, Cesare, Maarek, Yoelle, Tonellotto, Nicola, Silvestri, Fabrizio
Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information Retrieval (IR)
Externí odkaz:
http://arxiv.org/abs/2401.14887
In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnect
Externí odkaz:
http://arxiv.org/abs/2310.04875
Autor:
Bacciu, Andrea, Cuconasu, Florin, Siciliano, Federico, Silvestri, Fabrizio, Tonellotto, Nicola, Trappolini, Giovanni
Publikováno v:
CEUR Workshop Proceedings (2023, Vol. 3537, pp. 29-37)
The emergence of large language models (LLMs) has revolutionized machine learning and related fields, showcasing remarkable abilities in comprehending, generating, and manipulating human language. However, their conventional usage through API-based t
Externí odkaz:
http://arxiv.org/abs/2307.12798
Autor:
Bacciu, Andrea, Trappolini, Giovanni, Santilli, Andrea, Rodolà, Emanuele, Silvestri, Fabrizio
This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a si
Externí odkaz:
http://arxiv.org/abs/2306.14457
Graph Neural Networks (GNNs) have become essential for studying complex data, particularly when represented as graphs. Their value is underpinned by their ability to reflect the intricacies of numerous areas, ranging from social to biological network
Externí odkaz:
http://arxiv.org/abs/2306.00707
Publikováno v:
SIGIR 2023: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as
Externí odkaz:
http://arxiv.org/abs/2305.01447
Autor:
Trappolini, Giovanni, Maiorca, Valentino, Severino, Silvio, Rodolà, Emanuele, Silvestri, Fabrizio, Tolomei, Gabriele
Graph Neural Networks (GNNs) have proven to be successful in several predictive modeling tasks for graph-structured data. Amongst those tasks, link prediction is one of the fundamental problems for many real-world applications, such as recommender sy
Externí odkaz:
http://arxiv.org/abs/2209.09688