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...
Główni autorzy: | Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad |
---|---|
Format: | Artykuł |
Język: | English |
Wydane: |
IEEE
2023-01-01
|
Seria: | IEEE Access |
Hasła przedmiotowe: | |
Dostęp online: | https://ieeexplore.ieee.org/document/10131934/ |
Podobne zapisy
-
Fast Sampling of Score-Based Models With Cyclical Diffusion Sampling
od: Karimul Makhtidi, i wsp.
Wydane: (2024-01-01) -
On a generalization of fractional Langevin equation with boundary conditions
od: Zheng Kou, i wsp.
Wydane: (2022-01-01) -
Dynamical Sampling with Langevin Normalization Flows
od: Minghao Gu, i wsp.
Wydane: (2019-11-01) -
Calibration with confidence: a principled method for panel assessment
od: R. S. MacKay, i wsp.
Wydane: (2017-01-01) -
Lévy-walk-like Langevin dynamics
od: Xudong Wang, i wsp.
Wydane: (2019-01-01)