Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SMU: smooth activation function for deep networks using smoothing
  maximum technique   Deep learning researchers have a keen interest in proposing two new novel
activation functions which can boost network performance. A good choice of
activation function can have significant consequences in improving network
performance. A handcrafted activation is the most common choice in neural
network models. ReLU is the most common choice in the deep learning community
due to its simplicity though ReLU has some serious drawbacks. In this paper, we
have proposed a new novel activation function based on approximation of known
activation functions like Leaky ReLU, and we call this function Smooth Maximum
Unit (SMU). Replacing ReLU by SMU, we have got 6.22% improvement in the
CIFAR100 dataset with the ShuffleNet V2 model.

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The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph