Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Structured Memory based Deep Model to Detect as well as Characterize
  Novel Inputs   While deep learning has pushed the boundaries in various machine learning
tasks, the current models are still far away from replicating many functions
that a normal human brain can do. Explicit memorization based deep architecture
have been recently proposed with the objective to understand and predict
better. In this work, we design a system that involves a primary learner and an
adjacent representational memory bank which is organized using a comparative
learner. This spatially forked deep architecture with a structured memory can
simultaneously predict and reason about the nature of an input, which may even
belong to a category never seen in the training data, by relating it with the
memorized past representations at the higher layers. Characterizing images of
unseen object classes in both synthetic and real world datasets is used as an
example to showcase the operational success of the proposed framework.

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The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
2
The categories of this paper are: math
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th