Zobrazeno 1 - 10
of 3 507
pro vyhledávání: '"LA CAVA, A"'
Autor:
La Cava, Simone Maurizio, Concas, Sara, Tolosana, Ruben, Casula, Roberto, Orrù, Giulia, Drahansky, Martin, Fierrez, Julian, Marcialis, Gian Luca
3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions, such as the subject's distance from the camera or the camera's chara
Externí odkaz:
http://arxiv.org/abs/2409.10481
Autor:
La Cava, Lucio, Tagarelli, Andrea
Ensuring content compliance with community guidelines is crucial for maintaining healthy online social environments. However, traditional human-based compliance checking struggles with scaling due to the increasing volume of user-generated content an
Externí odkaz:
http://arxiv.org/abs/2409.08963
The significant progress in the development of Large Language Models has contributed to blurring the distinction between human and AI-generated text. The increasing pervasiveness of AI-generated text and the difficulty in detecting it poses new chall
Externí odkaz:
http://arxiv.org/abs/2407.09364
The global proliferation of social media platforms has transformed political communication, making the study of online interactions between politicians and voters crucial for understanding contemporary political discourse. In this work, we examine th
Externí odkaz:
http://arxiv.org/abs/2407.01279
Autor:
Lukyanenko, Platon, Mayourian, Joshua, Liu, Mingxuan, Triedman, John K., Ghelani, Sunil J., La Cava, William G.
Several recent high-impact studies leverage large hospital-owned electrocardiographic (ECG) databases to model and predict patient mortality. MIMIC-IV, released September 2023, is the first comparable public dataset and includes 800,000 ECGs from a U
Externí odkaz:
http://arxiv.org/abs/2406.17002
Verbs form the backbone of language, providing the structure and meaning to sentences. Yet, their intricate semantic nuances pose a longstanding challenge. Understanding verb relations through the concept of lexical entailment is crucial for comprehe
Externí odkaz:
http://arxiv.org/abs/2406.14894
Autor:
Chen, Shan, Gallifant, Jack, Gao, Mingye, Moreira, Pedro, Munch, Nikolaj, Muthukkumar, Ajay, Rajan, Arvind, Kolluri, Jaya, Fiske, Amelia, Hastings, Janna, Aerts, Hugo, Anthony, Brian, Celi, Leo Anthony, La Cava, William G., Bitterman, Danielle S.
Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data. In this study, we introduce Cross-Care, the first
Externí odkaz:
http://arxiv.org/abs/2405.05506
Publikováno v:
GSI Aldeia, FO de Fran\c{c}a, and WG La Cava. 2024. Minimum variance threshold for epsilon-lexicase selection. In Genetic and Evolutionary Computation Conference (GECCO '24)
Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the par
Externí odkaz:
http://arxiv.org/abs/2404.05909
Publikováno v:
GSI Aldeia, FO de Fran\c{c}a, WG La Cava. 2024. Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. In Genetic and Evolutionary Computation Conference (GECCO '24)
Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operati
Externí odkaz:
http://arxiv.org/abs/2404.05898
Autor:
Shahreza, Hatef Otroshi, Ecabert, Christophe, George, Anjith, Unnervik, Alexander, Marcel, Sébastien, Di Domenico, Nicolò, Borghi, Guido, Maltoni, Davide, Boutros, Fadi, Vogel, Julia, Damer, Naser, Sánchez-Pérez, Ángela, EnriqueMas-Candela, Calvo-Zaragoza, Jorge, Biesseck, Bernardo, Vidal, Pedro, Granada, Roger, Menotti, David, DeAndres-Tame, Ivan, La Cava, Simone Maurizio, Concas, Sara, Melzi, Pietro, Tolosana, Ruben, Vera-Rodriguez, Ruben, Perelli, Gianpaolo, Orrù, Giulia, Marcialis, Gian Luca, Fierrez, Julian
Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating syn
Externí odkaz:
http://arxiv.org/abs/2404.04580