Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Multiplicative Relations in Neural Networks   Universal approximation theorem suggests that a shallow neural network can
approximate any function. The input to neurons at each layer is a weighted sum
of previous layer neurons and then an activation is applied. These activation
functions perform very well when the output is a linear combination of input
data. When trying to learn a function which involves product of input data, the
neural networks tend to overfit the data to approximate the function. In this
paper we will use properties of logarithmic functions to propose a pair of
activation functions which can translate products into linear expression and
learn using backpropagation. We will try to generalize this approach for some
complex arithmetic functions and test the accuracy on a disjoint distribution
with the training set.

14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int