DeepAI AI Chat
Log In Sign Up

Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding

by   Ga Wu, et al.

Variational Autoencoders (VAEs) are a popular generative model, but one in which conditional inference can be challenging. If the decomposition into query and evidence variables is fixed, conditional VAEs provide an attractive solution. To support arbitrary queries, one is generally reduced to Markov Chain Monte Carlo sampling methods that can suffer from long mixing times. In this paper, we propose an idea we term cross-coding to approximate the distribution over the latent variables after conditioning on an evidence assignment to some subset of the variables. This allows generating query samples without retraining the full VAE. We experimentally evaluate three variations of cross-coding showing that (i) two can be quickly trained for different decompositions of evidence and query and (ii) they quantitatively and qualitatively outperform Hamiltonian Monte Carlo.


page 6

page 8

page 12


Improving Sampling from Generative Autoencoders with Markov Chains

We focus on generative autoencoders, such as variational or adversarial ...

Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps

Hamiltonian Monte Carlo (HMC) is a Markov chain algorithm for sampling f...

Invertible Flow Non Equilibrium sampling

Simultaneously sampling from a complex distribution with intractable nor...

Quasi-symplectic Langevin Variational Autoencoder

Variational autoencoder (VAE) as one of the well investigated generative...

Generalizing Hamiltonian Monte Carlo with Neural Networks

We present a general-purpose method to train Markov chain Monte Carlo ke...

Adaptive MCMC-Based Inference in Probabilistic Logic Programs

Probabilistic Logic Programming (PLP) languages enable programmers to sp...

Toward Unlimited Self-Learning Monte Carlo with Annealing Process Using VAE's Implicit Isometricity

Self-learning Monte Carlo (SLMC) methods are recently proposed to accele...

Code Repositories



view repo