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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Moura, J. & Santana, P. (2025). Toward end-to-end deep learning for autonomous management in next-generation networks. 2025 Sixteenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 181?186). IEEE. https://doi.org/10.1109/ICUFN65838.2025
Exportar Referência (IEEE) 
J. A. Moura and P. F. Santana,  "Toward end-to-end deep learning for autonomous management in next-generation networks", in  2025 Sixteenth Int. Conf. on Ubiquitous and Future Networks (ICUFN), Lisboa, IEEE, 2025, pp. 181-186
Exportar BibTeX 
@inproceedings{moura2025_1793627678889,
	author = "Moura, J. and Santana, P.",
	title = "Toward end-to-end deep learning for autonomous management in next-generation networks",
	booktitle = " 2025 Sixteenth International Conference on Ubiquitous and Future Networks (ICUFN)",
	year = "2025",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ICUFN65838.2025",
	pages = "181-186",
	publisher = "IEEE",
	address = "Lisboa",
	organization = "",
	url = "https://icufn.org/"
}
Exportar RIS 
TY  - CPAPER
TI  - Toward end-to-end deep learning for autonomous management in next-generation networks
T2  -  2025 Sixteenth International Conference on Ubiquitous and Future Networks (ICUFN)
AU  - Moura, J.
AU  - Santana, P.
PY  - 2025
SP  - 181-186
SN  - 2165-8528
DO  - 10.1109/ICUFN65838.2025
CY  - Lisboa
UR  - https://icufn.org/
AB  - The evolution towards next-generation of mobile networks demands for autonomous network management, emphasizing data-driven solutions based on Artificial Intelligence (AI), in particular machine learning. To attain such goals, this paper proposes a hybrid end-to-end learning approach that integrates imitation learning, deep reinforcement learning, simulation, domain adaptation, multi-agent cooperation, explainable AI, and generative AI. The work outlines a comprehensive vision for online agent learning about optimum network management policies while ensuring safety, interpretability, and adaptability in highly complex and dynamic use cases at the network periphery.
ER  -