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Faithful Heteroscedastic Regression with Neural Networks

Faithful Heteroscedastic Regression with Neural Networks

18 December 2022
Andrew Stirn
H. Wessels
Megan D. Schertzer
L. Pereira
Neville E. Sanjana
David A. Knowles
    UQCV
ArXivPDFHTML

Papers citing "Faithful Heteroscedastic Regression with Neural Networks"

12 / 12 papers shown
Title
PARIC: Probabilistic Attention Regularization for Language Guided Image Classification from Pre-trained Vison Language Models
Mayank Nautiyal
Stela Arranz Gheorghe
Kristiana Stefa
Li Ju
Ida-Maria Sintorn
Prashant Singh
VLM
56
0
0
14 Mar 2025
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla
Aziz Shameem
Mathieu Salzmann
Alexandre Alahi
39
1
0
14 Feb 2025
FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic
  Fairness
FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness
Lucas Rosenblatt
R. T. Witter
FaML
20
0
0
02 Oct 2024
Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty
  in Scientific Machine Learning
Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning
Farhad Pourkamali-Anaraki
Jamal F. Husseini
Scott E. Stapleton
UD
48
2
0
21 Feb 2024
Robust Estimation of Causal Heteroscedastic Noise Models
Robust Estimation of Causal Heteroscedastic Noise Models
Quang-Duy Tran
Bao Duong
Phuoc Nguyen
Thin Nguyen
19
1
0
15 Dec 2023
TIC-TAC: A Framework for Improved Covariance Estimation in Deep
  Heteroscedastic Regression
TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla
Mathieu Salzmann
Alexandre Alahi
19
3
0
29 Oct 2023
Understanding Pathologies of Deep Heteroskedastic Regression
Understanding Pathologies of Deep Heteroskedastic Regression
Eliot Wong-Toi
Alex Boyd
Vincent Fortuin
Stephan Mandt
UQCV
13
3
0
29 Jun 2023
Coherent Soft Imitation Learning
Coherent Soft Imitation Learning
Joe Watson
Sandy H. Huang
Nicholas Heess
30
11
0
25 May 2023
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical
  Satellite Time Series
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Patrick Ebel
Vivien Sainte Fare Garnot
M. Schmitt
Jan Dirk Wegner
Xiao Xiang Zhu
20
32
0
11 Apr 2023
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Uddeshya Upadhyay
Jae Myung Kim
Cordelia Schmidt
Bernhard Schölkopf
Zeynep Akata
BDL
UQCV
24
1
0
21 Feb 2023
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,660
0
05 Dec 2016
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
9,136
0
06 Jun 2015
1