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Epistemic Uncertainty Quantification For Pre-trained Neural Network

Epistemic Uncertainty Quantification For Pre-trained Neural Network

15 April 2024
Hanjing Wang
Qiang Ji
    UQCV
ArXivPDFHTML

Papers citing "Epistemic Uncertainty Quantification For Pre-trained Neural Network"

3 / 3 papers shown
Title
Ranking pre-trained segmentation models for zero-shot transferability
Joshua Talks
Anna Kreshuk
80
0
0
01 Mar 2025
On the Importance of Gradients for Detecting Distributional Shifts in
  the Wild
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang
Andrew Geng
Yixuan Li
173
326
0
01 Oct 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,109
0
06 Jun 2015
1