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Reducing Reparameterization Gradient Variance

Reducing Reparameterization Gradient Variance

22 May 2017
Andrew C. Miller
N. Foti
Alexander DÁmour
Ryan P. Adams
ArXivPDFHTML

Papers citing "Reducing Reparameterization Gradient Variance"

7 / 7 papers shown
Title
Provable convergence guarantees for black-box variational inference
Provable convergence guarantees for black-box variational inference
Justin Domke
Guillaume Garrigos
Robert Mansel Gower
18
18
0
04 Jun 2023
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated
  Learning
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Anran Li
Hongyi Peng
Lan Zhang
Jiahui Huang
Qing-Wu Guo
Han Yu
Yang Liu
FedML
15
28
0
21 Feb 2023
SG-VAD: Stochastic Gates Based Speech Activity Detection
SG-VAD: Stochastic Gates Based Speech Activity Detection
Jonathan Svirsky
Ofir Lindenbaum
24
4
0
28 Oct 2022
Latent Variable Modelling Using Variational Autoencoders: A survey
Latent Variable Modelling Using Variational Autoencoders: A survey
Vasanth Kalingeri
CML
DRL
18
2
0
20 Jun 2022
Deterministic Variational Inference for Robust Bayesian Neural Networks
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu
Sebastian Nowozin
Edward Meeds
Richard E. Turner
José Miguel Hernández-Lobato
Alexander L. Gaunt
UQCV
AAML
BDL
29
16
0
09 Oct 2018
Advances in Variational Inference
Advances in Variational Inference
Cheng Zhang
Judith Butepage
Hedvig Kjellström
Stephan Mandt
BDL
36
681
0
15 Nov 2017
Backpropagation through the Void: Optimizing control variates for
  black-box gradient estimation
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
Will Grathwohl
Dami Choi
Yuhuai Wu
Geoffrey Roeder
D. Duvenaud
36
299
0
31 Oct 2017
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