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...

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Dades bibliogràfiques
Autors principals:	Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad
Format:	Article
Idioma:	English
Publicat:	IEEE 2023-01-01
Col·lecció:	IEEE Access
Matèries:	Natural gradient second-order optimization Bayesian deep learning Langevin dynamics confidence calibration predictive uncertainty
Accés en línia:	https://ieeexplore.ieee.org/document/10131934/

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