Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joining datasets via data augmentation in the label space for neural
  networks   Most, if not all, modern deep learning systems restrict themselves to a
single dataset for neural network training and inference. In this article, we
are interested in systematic ways to join datasets that are made of similar
purposes. Unlike previous published works that ubiquitously conduct the dataset
joining in the uninterpretable latent vectorial space, the core to our method
is an augmentation procedure in the label space. The primary challenge to
address the label space for dataset joining is the discrepancy between labels:
non-overlapping label annotation sets, different labeling granularity or
hierarchy and etc. Notably we propose a new technique leveraging artificially
created knowledge graph, recurrent neural networks and policy gradient that
successfully achieve the dataset joining in the label space. Empirical results
on both image and text classification justify the validity of our approach.

29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int