ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.04910
  4. Cited By
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise
  Variance Parameterization

Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization

8 June 2020
Andrew Stirn
David A. Knowles
    DRL
ArXivPDFHTML

Papers citing "Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization"

4 / 4 papers shown
Title
Faithful Heteroscedastic Regression with Neural Networks
Faithful Heteroscedastic Regression with Neural Networks
Andrew Stirn
H. Wessels
Megan D. Schertzer
L. Pereira
Neville E. Sanjana
David A. Knowles
UQCV
30
14
0
18 Dec 2022
Structured Uncertainty in the Observation Space of Variational
  Autoencoders
Structured Uncertainty in the Observation Space of Variational Autoencoders
James A. G. Langley
M. Monteiro
Charles Jones
Nick Pawlowski
Ben Glocker
CML
OOD
BDL
DRL
39
2
0
25 May 2022
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and
  Supervised Lesion Detection
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection
H. Akrami
Anand A. Joshi
Sergul Aydore
Richard M. Leahy
UQCV
39
4
0
20 Sep 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
285
9,145
0
06 Jun 2015
1