Reducing Reparameterization Gradient Variance

22 May 2017

Papers citing "Reducing Reparameterization Gradient Variance"

10 / 10 papers shown

Title
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 Anran Li Hongyi Peng Lan Zhang Jiahui Huang Qing-Wu Guo Han Yu Yang Liu FedML 17 28 0 21 Feb 2023
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 Vasanth Kalingeri CML DRL 18 2 0 20 Jun 2022
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 14 29 0 19 Dec 2019
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
Pathwise Derivatives Beyond the Reparameterization Trick M. Jankowiak F. Obermeyer 14 110 0 05 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 9 30 0 01 Jun 2018
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 36 684 0 15 Nov 2017
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation Will Grathwohl Dami Choi Yuhuai Wu Geoffrey Roeder D. Duvenaud 36 300 0 31 Oct 2017