Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate Inference with Amortised MCMC   We propose a novel approximate inference algorithm that approximates a target
distribution by amortising the dynamics of a user-selected MCMC sampler. The
idea is to initialise MCMC using samples from an approximation network, apply
the MCMC operator to improve these samples, and finally use the samples to
update the approximation network thereby improving its quality. This provides a
new generic framework for approximate inference, allowing us to deploy highly
complex, or implicitly defined approximation families with intractable
densities, including approximations produced by warping a source of randomness
through a deep neural network. Experiments consider image modelling with deep
generative models as a challenging test for the method. Deep models trained
using amortised MCMC are shown to generate realistic looking samples as well as
producing diverse imputations for images with regions of missing pixels.

10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org