Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Any Variational Autoencoder Can Do Arbitrary Conditioning   Arbitrary conditioning is an important problem in unsupervised learning,
where we seek to model the conditional densities $p(\mathbf{x}_u \mid
\mathbf{x}_o)$ that underly some data, for all possible non-intersecting
subsets $o, u \subset \{1, \dots , d\}$. However, the vast majority of density
estimation only focuses on modeling the joint distribution $p(\mathbf{x})$, in
which important conditional dependencies between features are opaque. We
propose a simple and general framework, coined Posterior Matching, that enables
any Variational Autoencoder (VAE) to perform arbitrary conditioning, without
modification to the VAE itself. Posterior Matching applies to the numerous
existing VAE-based approaches to joint density estimation, thereby
circumventing the specialized models required by previous approaches to
arbitrary conditioning. We find that Posterior Matching achieves performance
that is comparable or superior to current state-of-the-art methods for a
variety of tasks.

25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex