Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Orthogonal-Pad\'e Activation Functions: Trainable Activation functions
  for smooth and faster convergence in deep networks   We have proposed orthogonal-Pad\'e activation functions, which are trainable
activation functions and show that they have faster learning capability and
improves the accuracy in standard deep learning datasets and models. Based on
our experiments, we have found two best candidates out of six orthogonal-Pad\'e
activations, which we call safe Hermite-Pade (HP) activation functions, namely
HP-1 and HP-2. When compared to ReLU, HP-1 and HP-2 has an increment in top-1
accuracy by 5.06% and 4.63% respectively in PreActResNet-34, by 3.02% and 2.75%
respectively in MobileNet V2 model on CIFAR100 dataset while on CIFAR10 dataset
top-1 accuracy increases by 2.02% and 1.78% respectively in PreActResNet-34, by
2.24% and 2.06% respectively in LeNet, by 2.15% and 2.03% respectively in
Efficientnet B0.

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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn