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Calibrating AI Models for Wireless Communications via Conformal Prediction

When used in complex engineered systems, such as communication networks, artificial intelligence (AI) models should be not only as accurate as possible, but also well calibrated. A well-calibrated AI model is one that can reliably quantify the uncertainty of its decisions, assigning high confidence...

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Principais autores: Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai Shitz
Format: Artigo
Sprog:Inglês
Udgivet: IEEE 2023-01-01
Serier:IEEE Transactions on Machine Learning in Communications and Networking
Fag:
Bayesian learning
calibration
conformal prediction
cross-validation
reliability
set prediction
Online adgang:https://ieeexplore.ieee.org/document/10262367/
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