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...
Main Authors: | Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad |
---|---|
פורמט: | Article |
שפה: | English |
יצא לאור: |
IEEE
2023-01-01
|
סדרה: | IEEE Access |
נושאים: | |
גישה מקוונת: | https://ieeexplore.ieee.org/document/10131934/ |
פריטים דומים
-
Fast Sampling of Score-Based Models With Cyclical Diffusion Sampling
מאת: Karimul Makhtidi, et al.
יצא לאור: (2024-01-01) -
On a generalization of fractional Langevin equation with boundary conditions
מאת: Zheng Kou, et al.
יצא לאור: (2022-01-01) -
Dynamical Sampling with Langevin Normalization Flows
מאת: Minghao Gu, et al.
יצא לאור: (2019-11-01) -
Calibration with confidence: a principled method for panel assessment
מאת: R. S. MacKay, et al.
יצא לאור: (2017-01-01) -
Lévy-walk-like Langevin dynamics
מאת: Xudong Wang, et al.
יצא לאור: (2019-01-01)