Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Class-conditional Autoencoders   Recent work by Bengio et al. (2013) proposes a sampling procedure for
denoising autoencoders which involves learning the transition operator of a
Markov chain. The transition operator is typically unimodal, which limits its
capacity to model complex data. In order to perform efficient sampling from
conditional distributions, we extend this work, both theoretically and
algorithmically, to gated autoencoders (Memisevic, 2013), The proposed model is
able to generate convincing class-conditional samples when trained on both the
MNIST and TFD datasets.

22
The categories of this paper are: chao-dyn
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
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph