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...
Auteurs principaux: | Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad |
---|---|
Format: | Article |
Langue: | English |
Publié: |
IEEE
2023-01-01
|
Collection: | IEEE Access |
Sujets: | |
Accès en ligne: | https://ieeexplore.ieee.org/document/10131934/ |
Documents similaires
-
Fast Sampling of Score-Based Models With Cyclical Diffusion Sampling
par: Karimul Makhtidi, et autres
Publié: (2024-01-01) -
On a generalization of fractional Langevin equation with boundary conditions
par: Zheng Kou, et autres
Publié: (2022-01-01) -
Dynamical Sampling with Langevin Normalization Flows
par: Minghao Gu, et autres
Publié: (2019-11-01) -
Calibration with confidence: a principled method for panel assessment
par: R. S. MacKay, et autres
Publié: (2017-01-01) -
Lévy-walk-like Langevin dynamics
par: Xudong Wang, et autres
Publié: (2019-01-01)