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. 2002.02655
  4. Cited By
The k-tied Normal Distribution: A Compact Parameterization of Gaussian
  Mean Field Posteriors in Bayesian Neural Networks
v1v2 (latest)

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
ArXiv (abs)PDFHTML

Papers citing "The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks"

26 / 26 papers shown
Title
Uncertainty Quantification in Alzheimer's Disease Progression Modeling
Uncertainty Quantification in Alzheimer's Disease Progression Modeling
Wael Mobeirek
Shirley Mao
OOD
29
0
0
13 Aug 2024
Unified Uncertainty Estimation for Cognitive Diagnosis Models
Unified Uncertainty Estimation for Cognitive Diagnosis Models
Fei-Yue Wang
Qi Liu
Enhong Chen
Chuanren Liu
Zhenya Huang
Jinze Wu
Shijin Wang
109
4
0
09 Mar 2024
Challenges in data-based geospatial modeling for environmental research
  and practice
Challenges in data-based geospatial modeling for environmental research and practice
Diana Koldasbayeva
P. Tregubova
M. Gasanov
Alexey Zaytsev
Anna Petrovskaia
Evgeny Burnaev
AI4CE
75
1
0
18 Nov 2023
How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To
  Quantify Uncertainty
How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty
Denis Mbey Akola
Gianni Franchi
ObjDUQCV
72
1
0
07 Oct 2023
A Survey on Uncertainty Quantification Methods for Deep Learning
A Survey on Uncertainty Quantification Methods for Deep Learning
Wenchong He
Zhe Jiang
Tingsong Xiao
Zelin Xu
Yukun Li
BDLUQCVAI4CE
193
24
0
26 Feb 2023
Improved uncertainty quantification for neural networks with Bayesian
  last layer
Improved uncertainty quantification for neural networks with Bayesian last layer
F. Fiedler
S. Lucia
UQCVBDL
125
14
0
21 Feb 2023
Variational Boosted Soft Trees
Variational Boosted Soft Trees
Tristan Cinquin
Tammo Rukat
Philipp Schmidt
Martin Wistuba
Artur Bekasov
BDLUQCV
61
0
0
21 Feb 2023
Variational Bayesian Neural Networks via Resolution of Singularities
Variational Bayesian Neural Networks via Resolution of Singularities
Susan Wei
Edmund Lau
BDL
69
2
0
13 Feb 2023
Flat Seeking Bayesian Neural Networks
Flat Seeking Bayesian Neural Networks
Van-Anh Nguyen
L. Vuong
Hoang Phan
Thanh-Toan Do
Dinh Q. Phung
Trung Le
BDL
100
10
0
06 Feb 2023
On the detrimental effect of invariances in the likelihood for
  variational inference
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle
R. Herbrich
Tim Januschowski
Bernie Wang
Jan Gasthaus
67
10
0
15 Sep 2022
Incorporating functional summary information in Bayesian neural networks
  using a Dirichlet process likelihood approach
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
Vishnu Raj
Tianyu Cui
Markus Heinonen
Pekka Marttinen
UQCVBDL
42
1
0
04 Jul 2022
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Yongdai Kim
TPM
53
1
0
02 Jun 2022
Pathologies in priors and inference for Bayesian transformers
Pathologies in priors and inference for Bayesian transformers
Tristan Cinquin
Alexander Immer
Max Horn
Vincent Fortuin
UQCVBDLMedIm
113
10
0
08 Oct 2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin
Mark Collier
F. Wenzel
J. Allingham
J. Liu
Dustin Tran
Balaji Lakshminarayanan
Jesse Berent
Rodolphe Jenatton
E. Kokiopoulou
UQCV
76
11
0
06 Oct 2021
TyXe: Pyro-based Bayesian neural nets for Pytorch
TyXe: Pyro-based Bayesian neural nets for Pytorch
H. Ritter
Theofanis Karaletsos
OODMUBDL
129
6
0
01 Oct 2021
Repulsive Deep Ensembles are Bayesian
Repulsive Deep Ensembles are Bayesian
Francesco DÁngelo
Vincent Fortuin
UQCVBDL
125
101
0
22 Jun 2021
On Stein Variational Neural Network Ensembles
On Stein Variational Neural Network Ensembles
Francesco DÁngelo
Vincent Fortuin
F. Wenzel
UQCVBDL
90
27
0
20 Jun 2021
Being a Bit Frequentist Improves Bayesian Neural Networks
Being a Bit Frequentist Improves Bayesian Neural Networks
Agustinus Kristiadi
Matthias Hein
Philipp Hennig
BDLUQCV
93
16
0
18 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
H. Ritter
Martin Kukla
Chen Zhang
Yingzhen Li
UQCVBDL
90
17
0
30 May 2021
Sampling-free Variational Inference for Neural Networks with
  Multiplicative Activation Noise
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
Jannik Schmitt
Stefan Roth
UQCV
55
6
0
15 Mar 2021
Structured Dropout Variational Inference for Bayesian Neural Networks
Structured Dropout Variational Inference for Bayesian Neural Networks
S. Nguyen
Duong Nguyen
Khai Nguyen
Khoat Than
Hung Bui
Nhat Ho
BDLDRL
58
8
0
16 Feb 2021
Bayesian Neural Network Priors Revisited
Bayesian Neural Network Priors Revisited
Vincent Fortuin
Adrià Garriga-Alonso
Sebastian W. Ober
F. Wenzel
Gunnar Rätsch
Richard Turner
Mark van der Wilk
Laurence Aitchison
BDLUQCV
133
141
0
12 Feb 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques,
  Applications and Challenges
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar
Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
Li Liu
...
Xiaochun Cao
Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
BDLUQCV
369
1,947
0
12 Nov 2020
Bayesian Deep Learning via Subnetwork Inference
Bayesian Deep Learning via Subnetwork Inference
Erik A. Daxberger
Eric T. Nalisnick
J. Allingham
Javier Antorán
José Miguel Hernández-Lobato
UQCVBDL
130
86
0
28 Oct 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
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
UQCVBDL
105
215
0
14 May 2020
Informative Bayesian Neural Network Priors for Weak Signals
Informative Bayesian Neural Network Priors for Weak Signals
Tianyu Cui
A. Havulinna
Pekka Marttinen
Samuel Kaski
57
9
0
24 Feb 2020
1