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Being Bayesian about Categorical Probability

Being Bayesian about Categorical Probability

19 February 2020
Taejong Joo
U. Chung
Minji Seo
    UQCV
    BDL
ArXivPDFHTML

Papers citing "Being Bayesian about Categorical Probability"

15 / 15 papers shown
Title
Optimizing Estimators of Squared Calibration Errors in Classification
Optimizing Estimators of Squared Calibration Errors in Classification
Sebastian G. Gruber
Francis Bach
69
1
0
24 Feb 2025
Subjective Logic Encodings
Subjective Logic Encodings
Jake Vasilakes
Chrysoula Zerva
Sophia Ananiadou
46
0
0
17 Feb 2025
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved
  Calibration and Model Selection in Unsupervised Domain Adaptation
IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptation
Taejong Joo
Diego Klabjan
33
1
0
16 Oct 2023
Overcoming Recency Bias of Normalization Statistics in Continual
  Learning: Balance and Adaptation
Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
Yilin Lyu
Liyuan Wang
Xingxing Zhang
Zicheng Sun
Hang Su
Jun Zhu
Liping Jing
34
8
0
13 Oct 2023
Discretization-Induced Dirichlet Posterior for Robust Uncertainty
  Quantification on Regression
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Xuanlong Yu
Gianni Franchi
Jindong Gu
Emanuel Aldea
UQCV
13
4
0
17 Aug 2023
Function-Space Regularization for Deep Bayesian Classification
Function-Space Regularization for Deep Bayesian Classification
J. Lin
Joe Watson
Pascal Klink
Jan Peters
UQCV
BDL
35
1
0
12 Jul 2023
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
Mixue Xie
Shuang Li
Rui Zhang
Chi Harold Liu
UQCV
30
29
0
27 Feb 2023
Revisiting Softmax for Uncertainty Approximation in Text Classification
Revisiting Softmax for Uncertainty Approximation in Text Classification
Andreas Nugaard Holm
Dustin Wright
Isabelle Augenstein
BDL
UQCV
14
8
0
25 Oct 2022
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
Thomas Joy
Francesco Pinto
Ser-Nam Lim
Philip H. S. Torr
P. Dokania
UQCV
19
30
0
13 Jul 2022
Switchable Representation Learning Framework with Self-compatibility
Switchable Representation Learning Framework with Self-compatibility
Shengsen Wu
Yan Bai
Yihang Lou
Xiongkun Linghu
Jianzhong He
Ling-yu Duan
22
1
0
16 Jun 2022
Robust Semantic Segmentation with Superpixel-Mix
Robust Semantic Segmentation with Superpixel-Mix
Gianni Franchi
Nacim Belkhir
Mai Lan Ha
Yufei Hu
Andrei Bursuc
V. Blanz
Angela Yao
UQCV
28
22
0
02 Aug 2021
A Survey of Uncertainty in Deep Neural Networks
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDL
UQCV
OOD
30
1,109
0
07 Jul 2021
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at
  Reliable OOD Detection
Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
Dennis Ulmer
Giovanni Cina
OODD
27
31
0
09 Dec 2020
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
270
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
279
9,136
0
06 Jun 2015
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