Reducing Reparameterization Gradient Variance

22 May 2017

Papers citing "Reducing Reparameterization Gradient Variance"

22 / 22 papers shown

Title
Fast and Unified Path Gradient Estimators for Normalizing Flows Lorenz Vaitl Ludwig Winkler Lorenz Richter Pan Kessel 44 4 0 23 Mar 2024
Provable convergence guarantees for black-box variational inference Justin Domke Guillaume Garrigos Robert Mansel Gower 25 18 0 04 Jun 2023
U-Statistics for Importance-Weighted Variational Inference Javier Burroni Kenta Takatsu Justin Domke Daniel Sheldon 18 1 0 27 Feb 2023
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning Anran Li Hongyi Peng Lan Zhang Jiahui Huang Qing Guo Han Yu Yang Liu FedML 39 28 0 21 Feb 2023
SG-VAD: Stochastic Gates Based Speech Activity Detection Jonathan Svirsky Ofir Lindenbaum 44 4 0 28 Oct 2022
Latent Variable Modelling Using Variational Autoencoders: A survey Vasanth Kalingeri CML DRL 26 2 0 20 Jun 2022
Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks Jurijs Nazarovs Ronak R. Mehta Vishnu Suresh Lokhande Vikas Singh UQCV BDL OOD 19 5 0 19 Feb 2022
Marginally calibrated response distributions for end-to-end learning in autonomous driving Clara Hoffmann Nadja Klein 11 2 0 03 Oct 2021
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 16 6 0 28 Mar 2020
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos T. Bui BDL 20 23 0 10 Feb 2020
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski G. Loaiza-Ganem John P. Cunningham 32 29 0 19 Dec 2019
The continuous Bernoulli: fixing a pervasive error in variational autoencoders G. Loaiza-Ganem John P. Cunningham DRL 29 83 0 16 Jul 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 45 397 0 25 Jun 2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation Justin Domke Daniel Sheldon 24 18 0 24 Jun 2019
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 29 16 0 09 Oct 2018
Quasi-Monte Carlo Variational Inference Alexander K. Buchholz F. Wenzel Stephan Mandt BDL 27 58 0 04 Jul 2018
Pathwise Derivatives Beyond the Reparameterization Trick M. Jankowiak F. Obermeyer 30 110 0 05 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 25 30 0 01 Jun 2018
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haussmann Fred Hamprecht M. Kandemir BDL 26 6 0 19 May 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen Paul Vicol Jimmy Ba Dustin Tran Roger C. Grosse BDL 22 307 0 12 Mar 2018
Advances in Variational Inference Cheng Zhang Judith Butepage Hedvig Kjellström Stephan Mandt BDL 38 684 0 15 Nov 2017
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation Will Grathwohl Dami Choi Yuhuai Wu Geoffrey Roeder David Duvenaud 56 300 0 31 Oct 2017