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Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation

Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation

Justin Domke
Daniel Sheldon
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Papers citing "Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation"

6 / 6 papers shown
Title
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
Yaniv Yacoby
Weiwei Pan
Finale Doshi-Velez
DRL
29
0
0
13 Mar 2024
eVAE: Evolutionary Variational Autoencoder
eVAE: Evolutionary Variational Autoencoder
Zhangkai Wu
LongBing Cao
Lei Qi
BDL
DRL
33
10
0
01 Jan 2023
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Tomas Geffner
Justin Domke
29
5
0
08 Mar 2022
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
MCMC Variational Inference via Uncorrected Hamiltonian Annealing
Tomas Geffner
Justin Domke
33
34
0
08 Jul 2021
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Abhinav Agrawal
Daniel Sheldon
Justin Domke
TPM
BDL
8
38
0
18 Jun 2020
Markovian Score Climbing: Variational Inference with KL(p||q)
Markovian Score Climbing: Variational Inference with KL(p||q)
C. A. Naesseth
Fredrik Lindsten
David M. Blei
123
54
0
23 Mar 2020