Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Document Neural Autoregressive Distribution Estimation   We present an approach based on feed-forward neural networks for learning the
distribution of textual documents. This approach is inspired by the Neural
Autoregressive Distribution Estimator(NADE) model, which has been shown to be a
good estimator of the distribution of discrete-valued igh-dimensional vectors.
In this paper, we present how NADE can successfully be adapted to the case of
textual data, retaining from NADE the property that sampling or computing the
probability of observations can be done exactly and efficiently. The approach
can also be used to learn deep representations of documents that are
competitive to those learned by the alternative topic modeling approaches.
Finally, we describe how the approach can be combined with a regular neural
network N-gram model and substantially improve its performance, by making its
learned representation sensitive to the larger, document-specific context.

32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg