Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimizing Binary Symptom Checkers via Approximate Message Passing   Symptom checkers have been widely adopted as an intelligent e-healthcare
application during the ongoing pandemic crisis. Their performance have been
limited by the fine-grained quality of the collected medical knowledge between
symptom and diseases. While the binarization of the relationships between
symptoms and diseases simplifies the data collection process, it also leads to
non-convex optimization problems during the inference step. In this paper, we
formulate the symptom checking problem as an underdertermined non-convex
optimization problem, thereby justifying the use of the compressive sensing
framework to solve it. We show that the generalized vector approximate message
passing (G-VAMP) algorithm provides the best performance for binary symptom
checkers.

27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ