Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Estimation of Distributions   This paper presents the recurrent estimation of distributions (RED) for
modeling real-valued data in a semiparametric fashion. RED models make two
novel uses of recurrent neural networks (RNNs) for density estimation of
general real-valued data. First, RNNs are used to transform input covariates
into a latent space to better capture conditional dependencies in inputs.
After, an RNN is used to compute the conditional distributions of the latent
covariates. The resulting model is efficient to train, compute, and sample
from, whilst producing normalized pdfs. The effectiveness of RED is shown via
several real-world data experiments. Our results show that RED models achieve a
lower held-out negative log-likelihood than other neural network approaches
across multiple dataset sizes and dimensionalities. Further context of the
efficacy of RED is provided by considering anomaly detection tasks, where we
also observe better performance over alternative models.

2
The categories of this paper are: math
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org