We propose a simple and scalable method for improving the flexibility of variational inference through a transformation with autoregressive networks. Autoregressive networks, such as RNNs and MADE Germain et al. (2015), are very powerful models; however, ancestral sampling in such networks is a sequential operation, therefore unappealing for direct use as approximate posteriors in variational inference on parallel hardware such as GPUs. We find that by inverting autoregressive networks we can obtain equally powerful data transformations that can often be computed in parallel. We show that such data transformations, inverse autoregressive flows (IAF), can be used to transform a simple distribution over the latent variables into a much more flexible distribution, while still allowing us to compute the resulting variables' probability density function. The method is simple to implement, can be made arbitrarily flexible, and (in contrast with previous work) is naturally applicable to latent variables that are organized in multidimensional tensors, such as 2D grids or time series. The method is applied to a novel deep architecture of variational auto-encoders. In experiments we demonstrate that autoregressive flow leads to significant performance gains when applied to variational autoencoders for natural images.
Inverse Autoregressive Flows REF use autoregressive structures in the latent space to learn more flexible posteriors for variational autoencoders.
Improving Variational Inference with Inverse Autoregressive Flow
ArXiv
ArXiv
Computer Science Mathematics