Zobrazeno 1 - 10
of 389
pro vyhledávání: '"Giannotti Fosca"'
Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences. Traditional methods often aggregate multiple annotations into a single ground trut
Externí odkaz:
http://arxiv.org/abs/2411.08752
Autor:
Gambetta, Daniele, Gezici, Gizem, Giannotti, Fosca, Pedreschi, Dino, Knott, Alistair, Pappalardo, Luca
Recent research has focused on the medium and long-term impacts of generative AI, posing scientific and societal challenges mainly due to the detection and reliability of machine-generated information, which is projected to form the major content on
Externí odkaz:
http://arxiv.org/abs/2410.12341
Autor:
Pappalardo, Luca, Ferragina, Emanuele, Citraro, Salvatore, Cornacchia, Giuliano, Nanni, Mirco, Rossetti, Giulio, Gezici, Gizem, Giannotti, Fosca, Lalli, Margherita, Gambetta, Daniele, Mauro, Giovanni, Morini, Virginia, Pansanella, Valentina, Pedreschi, Dino
Recommendation systems and assistants (in short, recommenders) are ubiquitous in online platforms and influence most actions of our day-to-day lives, suggesting items or providing solutions based on users' preferences or requests. This survey analyse
Externí odkaz:
http://arxiv.org/abs/2407.01630
Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models eve
Externí odkaz:
http://arxiv.org/abs/2402.06287
Autor:
Tripto, Nafis Irtiza, Uchendu, Adaku, Le, Thai, Setzu, Mattia, Giannotti, Fosca, Lee, Dongwon
Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language Models (LLMs) has made authorship analysis increasingly crucial for d
Externí odkaz:
http://arxiv.org/abs/2310.16746
Autor:
Pedreschi, Dino, Pappalardo, Luca, Ferragina, Emanuele, Baeza-Yates, Ricardo, Barabasi, Albert-Laszlo, Dignum, Frank, Dignum, Virginia, Eliassi-Rad, Tina, Giannotti, Fosca, Kertesz, Janos, Knott, Alistair, Ioannidis, Yannis, Lukowicz, Paul, Passarella, Andrea, Pentland, Alex Sandy, Shawe-Taylor, John, Vespignani, Alessandro
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender syst
Externí odkaz:
http://arxiv.org/abs/2306.13723
Synthetic data generation has been widely adopted in software testing, data privacy, imbalanced learning, and artificial intelligence explanation. In all such contexts, it is crucial to generate plausible data samples. A common assumption of approach
Externí odkaz:
http://arxiv.org/abs/2301.07427
Autor:
Agliari, Elena, Albanese, Linda, Alemanno, Francesco, Alessandrelli, Andrea, Barra, Adriano, Giannotti, Fosca, Lotito, Daniele, Pedreschi, Dino
We consider dense, associative neural-networks trained by a teacher (i.e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations. In
Externí odkaz:
http://arxiv.org/abs/2212.00606
Autor:
Agliari, Elena, Albanese, Linda, Alemanno, Francesco, Alessandrelli, Andrea, Barra, Adriano, Giannotti, Fosca, Lotito, Daniele, Pedreschi, Dino
We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations. In particular, we obtain
Externí odkaz:
http://arxiv.org/abs/2211.14067
Publikováno v:
ACM Computing Surveys, Volume 56, Issue 7, Article No. 171, pp 1-37, 2024
Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in grap
Externí odkaz:
http://arxiv.org/abs/2210.12089