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...
Κύριοι συγγραφείς: | Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad |
---|---|
Μορφή: | Άρθρο |
Γλώσσα: | English |
Έκδοση: |
IEEE
2023-01-01
|
Σειρά: | IEEE Access |
Θέματα: | |
Διαθέσιμο Online: | https://ieeexplore.ieee.org/document/10131934/ |
Παρόμοια τεκμήρια
-
Fast Sampling of Score-Based Models With Cyclical Diffusion Sampling
ανά: Karimul Makhtidi, κ.ά.
Έκδοση: (2024-01-01) -
On a generalization of fractional Langevin equation with boundary conditions
ανά: Zheng Kou, κ.ά.
Έκδοση: (2022-01-01) -
Dynamical Sampling with Langevin Normalization Flows
ανά: Minghao Gu, κ.ά.
Έκδοση: (2019-11-01) -
Calibration with confidence: a principled method for panel assessment
ανά: R. S. MacKay, κ.ά.
Έκδοση: (2017-01-01) -
Lévy-walk-like Langevin dynamics
ανά: Xudong Wang, κ.ά.
Έκδοση: (2019-01-01)