Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Contextual Anomaly Detection using Joint Deep Variational
  Generative Models   A method for unsupervised contextual anomaly detection is proposed using a
cross-linked pair of Variational Auto-Encoders for assigning a normality score
to an observation. The method enables a distinct separation of contextual from
behavioral attributes and is robust to the presence of anomalous or novel
contextual attributes. The method can be trained with data sets that contain
anomalies without any special pre-processing.

15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess