Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism.  
However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior.  
While it is possible to use normalizing flow approximate posteriors for continuous latents, there is nothing analogous for discrete latents.
The most natural approach to model discrete dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow.  
We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space models.  
To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow.  
We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach.  
We tested our approach on the neuroscience problem of inferring discrete spiking activity from noisy calcium-imaging data, and found that it gave accurate connectivity estimates in an order of magnitude less time.
We have described an approach to sampling from a discrete autoregressive distribution using a parallel, flow-like procedure, derived by considering fixed-point iterations that converge to a sample from the underlying autoregressive process.
We applied this procedure to speed up sampling from autoregressive approximate posteriors in the variational inference training loop.
This allowed us to rapidly learn autoregressive posteriors in the context of neural data analysis, allowing us to realise the benefits of autoregressive approximate posteriors for single and multi cell data in reasonable timescales.It is important to remember that while we can sample using K fixed-point iterations, we can only evaluate the probability of a sample once it has converged.
This mismatch introduces a level of approximation in addition to those that are typical when relaxing discrete distributions BID8 BID11 ), but we can deal with the additional approximation error in the same way: by evaluating the model using samples drawn from the underlying discrete, autoregressive approximate posterior.Past work has used similar properties of the underlying generative model, to speed up messagepassing based inference algorithms BID6 BID4 .
It is likely that their approach will be preferable when exact inference is possible albeit costly due to large tree-width/timecourses, whereas our approach will be preferable when exact inference is not possible due to longrange temporal dependencies.Finally, our work suggests two directions for future work.
First, while it is possible to use normalizing flows to define approximate posteriors for continuous state-space models, it may be difficult to know exactly which normalizing flow will prove most effective.
In this context, our procedure of using fixed-point iterations may be a useful starting point.
Second, we showed that while it may be possible to convert a discrete latent variable model to an equivalent model with continuous latents, this typically introduces considerable scope for mismatch between the prior and approximate posterior.
However, the actual approximate posterior is relatively simple, a mixture of truncated Logistics, and as such, it may be possible to design approximate posteriors or even whole relaxation schemes that more closely match the true posterior, and indeed this may underlie the gains shown by BID17 .
We give a fast normalising-flow like sampling procedure for discrete latent variable models.