Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Lossy Autoencoder   Representation learning seeks to expose certain aspects of observed data in a
learned representation that's amenable to downstream tasks like classification.
For instance, a good representation for 2D images might be one that describes
only global structure and discards information about detailed texture. In this
paper, we present a simple but principled method to learn such global
representations by combining Variational Autoencoder (VAE) with neural
autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE
model allows us to have control over what the global latent code can learn and
, by designing the architecture accordingly, we can force the global latent
code to discard irrelevant information such as texture in 2D images, and hence
the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging
autoregressive models as both prior distribution $p(z)$ and decoding
distribution $p(x|z)$, we can greatly improve generative modeling performance
of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and
Caltech-101 Silhouettes density estimation tasks.

24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int