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Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series
  Prediction and Inference

Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference

17 January 2019
Xinyu Hu
Paul A. Szerlip
Theofanis Karaletsos
Rohit Singh
    BDLAI4TS
ArXiv (abs)PDFHTML

Papers citing "Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference"

7 / 7 papers shown
Title
Deep and Confident Prediction for Time Series at Uber
Deep and Confident Prediction for Time Series at Uber
Lingxue Zhu
N. Laptev
BDLAI4TS
156
345
0
06 Sep 2017
Stein Variational Gradient Descent as Gradient Flow
Stein Variational Gradient Descent as Gradient Flow
Qiang Liu
OT
80
276
0
25 Apr 2017
Stein Variational Gradient Descent: A General Purpose Bayesian Inference
  Algorithm
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu
Dilin Wang
BDL
73
1,092
0
16 Aug 2016
A Theoretically Grounded Application of Dropout in Recurrent Neural
  Networks
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Y. Gal
Zoubin Ghahramani
UQCVDRLBDL
187
1,650
0
16 Dec 2015
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
UQCVBDL
829
9,318
0
06 Jun 2015
Weight Uncertainty in Neural Networks
Weight Uncertainty in Neural Networks
Charles Blundell
Julien Cornebise
Koray Kavukcuoglu
Daan Wierstra
UQCVBDL
187
1,887
0
20 May 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural
  Networks
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
José Miguel Hernández-Lobato
Ryan P. Adams
UQCVBDL
130
946
0
18 Feb 2015
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