119 citations · 170 across the 5 of their papers we have counts for
4 papers · 1 filter
NVAE: A Deep Hierarchical Variational Autoencoder
Arash Vahdat, Jan Kautz
Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning…
Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William G. Macready
The representation of the approximate posterior is a critical aspect of effective variational autoencoders (VAEs). Poor choices for the approximate posterior have a detrimental imp…
Improved Gradient-Based Optimization Over Discrete Distributions
Evgeny Andriyash, Arash Vahdat, Bill Macready
In many applications we seek to maximize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribu…
DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
Arash Vahdat, Evgeny Andriyash, William G. Macready
Boltzmann machines are powerful distributions that have been shown to be an effective prior over binary latent variables in variational autoencoders (VAEs). However, previous metho…