Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NNDrone: a toolkit for the mass application of machine learning in High
  Energy Physics   Machine learning has proven to be an indispensable tool in the selection of
interesting events in high energy physics. Such technologies will become
increasingly important as detector upgrades are introduced and data rates
increase by orders of magnitude. We propose a toolkit to enable the creation of
a drone classifier from any machine learning classifier, such that different
classifiers may be standardised into a single form and executed in parallel. We
demonstrate the capability of the drone neural network to learn the required
properties of the input neural network without the use of any labels from the
training data, only using appropriate questioning of the input neural network.

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The categories of this paper are: hep-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat