Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Peek Into the Hidden Layers of a Convolutional Neural Network Through
  a Factorization Lens   Despite their increasing popularity and success in a variety of supervised
learning problems, deep neural networks are extremely hard to interpret and
debug: Given and already trained Deep Neural Net, and a set of test inputs, how
can we gain insight into how those inputs interact with different layers of the
neural network? Furthermore, can we characterize a given deep neural network
based on it's observed behavior on different inputs? In this paper we propose a
novel factorization based approach on understanding how different deep neural
networks operate. In our preliminary results, we identify fascinating patterns
that link the factorization rank (typically used as a measure of
interestingness in unsupervised data analysis) with how well or poorly the deep
network has been trained. Finally, our proposed approach can help provide
visual insights on how high-level. interpretable patterns of the network's
input behave inside the hidden layers of the deep network.

37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin