Multiple Kernel Subspace Learning for Clustering and Classification

Chi, Ziqiu; Wang, Zhe; Wang, Bolu; Fang, Zhongli; Zhu, Zonghai; Li, Dongdong; Du, Wenli

doi:10.1109/tkde.2022.3200723

Cited by 9 publications

(1 citation statement)

References 47 publications

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“…This transfer learning approach leverages a minimal amount of information, including definitions, descriptions, examples, and behavior specifications (e.g. network engineer), as prompts [20]. These prompts are employed to fine-tune and customize the models, avoiding the need to adjust the model's entire parameter set.…”

Section: B Large Language Modelmentioning

confidence: 99%

Towards Intent-based Network Management for the 6G System adopting Multimodal Generative AI

Brodimas,

Trantzas,

Agko

et al. 2024

2024 Joint European Conference on Networks and Communications &Amp;amp; 6G Summit (EuCNC/6G Summit)

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The emerging concept of delivering Network-as-a-Service (NaaS) foresees the deployment and reconfiguration of the next-generation networks, such as 6G, in a dynamic and elastic manner, tailored to the respective stakeholder's intention. Taking this into account, the efficient management and orchestration of both telecommunication and computational resources across the network domains, i.e. access, transport and core presents a considerable challenge, even for network experts. To tackle this complexity, this paper explores the implementation of an intentbased management framework. The framework receives a highlevel description of the desired network capabilities along with supplementary files, e.g. deployment descriptors, and translates them into configuration files consumable by the network itself. In order to achieve this, the paper establishes a translation pipeline that leverages the employment of emerging multimodal generative artificial intelligence (GenAI) models, specifically Large Language Models (LLMs), and open industry-ready standard templates. The adoption of those two emerging technologies offers high dynamicity on the interpretation process of the user's intent, while ensuring that its outcome is compatible with every orchestrator or next-generation Operating Support System (Next-gen OSS) that adheres to those standards.

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Section: B Large Language Modelmentioning

confidence: 99%