Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A parameterized activation function for learning fuzzy logic operations
  in deep neural networks   We present a deep learning architecture for learning fuzzy logic expressions.
Our model uses an innovative, parameterized, differentiable activation function
that can learn a number of logical operations by gradient descent. This
activation function allows a neural network to determine the relationships
between its input variables and provides insight into the logical significance
of learned network parameters. We provide a theoretical basis for this
parameterization and demonstrate its effectiveness and utility by successfully
applying our model to five classification problems from the UCI Machine
Learning Repository.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph