Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications-Reference-Cited by-同舟云学术

Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications

Author:

Zecchin Matteo¹^ORCID,Park Sangwoo¹^ORCID,Simeone Osvaldo¹^ORCID,Kountouris Marios²,Gesbert David²

Affiliation:

1. Department of Engineering, King’s Communications, Learning & Information Processing Lab, King’s College London, London, U.K

2. Communication Systems Department, EURECOM, Sophia-Antipolis, France

Funder

Marie Curie action WINDMILL

European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Link

Reference80 articles.

2. Artificial intelligence in open radio access network;masur;arXiv 2104 09445,2021

4. BAMLD: Bayesian active meta-learning by disagreement;nikoloska;arXiv 2110 09943,2021

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