Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks   We propose to learn a curriculum or a syllabus for supervised learning and
deep reinforcement learning with deep neural networks by an attachable deep
neural network, called ScreenerNet. Specifically, we learn a weight for each
sample by jointly training the ScreenerNet and the main network in an
end-to-end self-paced fashion. The ScreenerNet neither has sampling bias nor
requires to remember the past learning history. We show the networks augmented
with the ScreenerNet achieve early convergence with better accuracy than the
state-of-the-art curricular learning methods in extensive experiments using
three popular vision datasets such as MNIST, CIFAR10 and Pascal VOC2012, and a
Cart-pole task using Deep Q-learning. Moreover, the ScreenerNet can extend
other curriculum learning methods such as Prioritized Experience Replay (PER)
for further accuracy improvement.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: stat
20
The categories of this paper are: acc-phys
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The categories of this paper are: eess
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
2
The categories of this paper are: math