Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Masking schemes for universal marginalisers   We consider the effect of structure-agnostic and structure-dependent masking
schemes when training a universal marginaliser (arXiv:1711.00695) in order to
learn conditional distributions of the form $P(x_i |\mathbf x_{\mathbf b})$,
where $x_i$ is a given random variable and $\mathbf x_{\mathbf b}$ is some
arbitrary subset of all random variables of the generative model of interest.
In other words, we mimic the self-supervised training of a denoising
autoencoder, where a dataset of unlabelled data is used as partially observed
input and the neural approximator is optimised to minimise reconstruction loss.
We focus on studying the underlying process of the partially observed
data---how good is the neural approximator at learning all conditional
distributions when the observation process at prediction time differs from the
masking process during training? We compare networks trained with different
masking schemes in terms of their predictive performance and generalisation
properties.

31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat