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2002.02655
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The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
7 February 2020
J. Swiatkowski
Kevin Roth
Bastiaan S. Veeling
Linh-Tam Tran
Joshua V. Dillon
Jasper Snoek
Stephan Mandt
Tim Salimans
Rodolphe Jenatton
Sebastian Nowozin
BDL
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Papers citing
"The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks"
26 / 26 papers shown
Title
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Challenges in data-based geospatial modeling for environmental research and practice
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How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty
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07 Oct 2023
A Survey on Uncertainty Quantification Methods for Deep Learning
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26 Feb 2023
Improved uncertainty quantification for neural networks with Bayesian last layer
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Variational Bayesian Neural Networks via Resolution of Singularities
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69
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Flat Seeking Bayesian Neural Networks
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On the detrimental effect of invariances in the likelihood for variational inference
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Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
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42
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04 Jul 2022
Masked Bayesian Neural Networks : Computation and Optimality
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53
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02 Jun 2022
Pathologies in priors and inference for Bayesian transformers
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Deep Classifiers with Label Noise Modeling and Distance Awareness
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76
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06 Oct 2021
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OOD
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129
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Repulsive Deep Ensembles are Bayesian
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Vincent Fortuin
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On Stein Variational Neural Network Ensembles
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Vincent Fortuin
F. Wenzel
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20 Jun 2021
Being a Bit Frequentist Improves Bayesian Neural Networks
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Matthias Hein
Philipp Hennig
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Sparse Uncertainty Representation in Deep Learning with Inducing Weights
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Martin Kukla
Chen Zhang
Yingzhen Li
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0
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Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
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Stefan Roth
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55
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0
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Structured Dropout Variational Inference for Bayesian Neural Networks
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Duong Nguyen
Khai Nguyen
Khoat Than
Hung Bui
Nhat Ho
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58
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0
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Bayesian Neural Network Priors Revisited
Vincent Fortuin
Adrià Garriga-Alonso
Sebastian W. Ober
F. Wenzel
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Laurence Aitchison
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133
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A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
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Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
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Abbas Khosravi
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V. Makarenkov
S. Nahavandi
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369
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12 Nov 2020
Bayesian Deep Learning via Subnetwork Inference
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Eric T. Nalisnick
J. Allingham
Javier Antorán
José Miguel Hernández-Lobato
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130
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0
28 Oct 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W. Dusenberry
Ghassen Jerfel
Yeming Wen
Yi-An Ma
Jasper Snoek
Katherine A. Heller
Balaji Lakshminarayanan
Dustin Tran
UQCV
BDL
105
215
0
14 May 2020
Informative Bayesian Neural Network Priors for Weak Signals
Tianyu Cui
A. Havulinna
Pekka Marttinen
Samuel Kaski
57
9
0
24 Feb 2020
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