Zobrazeno 1 - 10
of 6 597
pro vyhledávání: '"Bovet A"'
Publikováno v:
Journal of the Serbian Chemical Society, Vol 88, Iss 11, Pp 1135-1147 (2023)
The electrical conductivity of LiCl–GdCl3 molten systems with the gadolinium chloride additions ranging from 0 to 23 mol % was measured depending on both the temperature and concentration of GdCl3. The molar conductivity of the molten GdCl3–LiCl
Externí odkaz:
https://doaj.org/article/7182885a2e054bb0b0d893418e0cfb96
Autor:
Rehmann, Andrin, Bovet, Alexandre
Signed graphs allow for encoding positive and negative relations between nodes and are used to model various online activities. Node representation learning for signed graphs is a well-studied task with important applications such as sign prediction.
Externí odkaz:
http://arxiv.org/abs/2412.12916
Autor:
Sánchez, Pedro Miguel Sánchez, Beltrán, Enrique Tomás Martínez, Llamas, Miguel Fernández, Bovet, Gérôme, Pérez, Gregorio Martínez, Celdrán, Alberto Huertas
Decentralized Federated Learning (DFL) trains models in a collaborative and privacy-preserving manner while removing model centralization risks and improving communication bottlenecks. However, DFL faces challenges in efficient communication manageme
Externí odkaz:
http://arxiv.org/abs/2412.11207
Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about
Externí odkaz:
http://arxiv.org/abs/2410.19651
Autor:
Feng, Chao, Guan, Hongjie, Celdrán, Alberto Huertas, von der Assen, Jan, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness. The exist
Externí odkaz:
http://arxiv.org/abs/2410.07678
Autor:
Celdrán, Alberto Huertas, Feng, Chao, Banik, Sabyasachi, Bovet, Gerome, Perez, Gregorio Martinez, Stiller, Burkhard
Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been ex
Externí odkaz:
http://arxiv.org/abs/2410.06127
Autor:
Feng, Chao, Celdrán, Alberto Huertas, Zeng, Zien, Ye, Zi, von der Assen, Jan, Bovet, Gerome, Stiller, Burkhard
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently an
Externí odkaz:
http://arxiv.org/abs/2409.19302
Selective exposure, individuals' inclination to seek out information that supports their beliefs while avoiding information that contradicts them, plays an important role in the emergence of polarization. In the political domain, selective exposure i
Externí odkaz:
http://arxiv.org/abs/2408.03828
Autor:
Feng, Chao, Celdrán, Alberto Huertas, von der Assen, Jan, Beltrán, Enrique Tomás Martínez, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central s
Externí odkaz:
http://arxiv.org/abs/2407.08652
Prompting and Multiple Choices Questions (MCQ) have become the preferred approach to assess the capabilities of Large Language Models (LLMs), due to their ease of manipulation and evaluation. Such experimental appraisals have pointed toward the LLMs'
Externí odkaz:
http://arxiv.org/abs/2406.14986