Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable Factorization for Neural Network ECG Models   The ability of deep learning (DL) to improve the practice of medicine and its
clinical outcomes faces a looming obstacle: model interpretation. Without
description of how outputs are generated, a collaborating physician can neither
resolve when the model's conclusions are in conflict with his or her own, nor
learn to anticipate model behavior. Current research aims to interpret networks
that diagnose ECG recordings, which has great potential impact as recordings
become more personalized and widely deployed. A generalizable impact beyond
ECGs lies in the ability to provide a rich test-bed for the development of
interpretive techniques in medicine. Interpretive techniques for Deep Neural
Networks (DNNs), however, tend to be heuristic and observational in nature,
lacking the mathematical rigor one might expect in the analysis of math
equations. The motivation of this paper is to offer a third option, a
scientific approach. We treat the model output itself as a phenomenon to be
explained through component parts and equations governing their behavior. We
argue that these component parts should also be "black boxes" --additional
targets to interpret heuristically with clear functional connection to the
original. We show how to rigorously factor a DNN into a hierarchical equation
consisting of black box variables. This is not a subdivision into physical
parts, like an organism into its cells; it is but one choice of an equation
into a collection of abstract functions. Yet, for DNNs trained to identify
normal ECG waveforms on PhysioNet 2017 Challenge data, we demonstrate this
choice yields interpretable component models identified with visual composite
sketches of ECG samples in corresponding input regions. Moreover, the recursion
distills this interpretation: additional factorization of component black boxes
corresponds to ECG partitions that are more morphologically pure.

8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con