Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Auxiliary Network: Scalable and agile online learning for dynamic system
  with inconsistently available inputs   Streaming classification methods assume the number of input features is fixed
and always received. But in many real-world scenarios demand is some input
features are reliable while others are unreliable or inconsistent. In this
paper, we propose a novel deep learning-based model called Auxiliary Network
(Aux-Net), which is scalable and agile. It employs a weighted ensemble of
classifiers to give a final outcome. The Aux-Net model is based on the hedging
algorithm and online gradient descent. It employs a model of varying depth in
an online setting using single pass learning. Aux-Net is a foundational work
towards scalable neural network model for a dynamic complex environment
requiring ad hoc or inconsistent input data. The efficacy of Aux-Net is shown
on public dataset.

12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol