Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Neural Decomposition to Explain Multivariate Data Sets   Investigating relationships between variables in multi-dimensional data sets
is a common task for data analysts and engineers. More specifically, it is
often valuable to understand which ranges of which input variables lead to
particular values of a given target variable. Unfortunately, with an increasing
number of independent variables, this process may become cumbersome and
time-consuming due to the many possible combinations that have to be explored.
In this paper, we propose a novel approach to visualize correlations between
input variables and a target output variable that scales to hundreds of
variables. We developed a visual model based on neural networks that can be
explored in a guided way to help analysts find and understand such
correlations. First, we train a neural network to predict the target from the
input variables. Then, we visualize the inner workings of the resulting model
to help understand relations within the data set. We further introduce a new
regularization term for the backpropagation algorithm that encourages the
neural network to learn representations that are easier to interpret visually.
We apply our method to artificial and real-world data sets to show its utility.

24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph