On Performance and Calibration of Natural Gradient Langevin Dynamics

On Performance and Calibration of Natural Gradient Langevin Dynamics

Producing deep neural network (DNN) models with calibrated confidence is essential for applications in many fields, such as medical image analysis, natural language processing, and robotics. Modern neural networks have been reported to be poorly calibrated compared with those from a decade ago. The...

Полное описание

Библиографические подробности
Главные авторы:	Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad
Формат:	Статья
Язык:	English
Опубликовано:	IEEE 2023-01-01
Серии:	IEEE Access
Предметы:	Natural gradient second-order optimization Bayesian deep learning Langevin dynamics confidence calibration predictive uncertainty
Online-ссылка:	https://ieeexplore.ieee.org/document/10131934/

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