Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human-in-the-loop online multi-agent approach to increase
  trustworthiness in ML models through trust scores and data augmentation   Increasing a ML model accuracy is not enough, we must also increase its
trustworthiness. This is an important step for building resilient AI systems
for safety-critical applications such as automotive, finance, and healthcare.
For that purpose, we propose a multi-agent system that combines both machine
and human agents. In this system, a checker agent calculates a trust score of
each instance (which penalizes overconfidence and overcautiousness in
predictions) using an agreement-based method and ranks it; then an improver
agent filters the anomalous instances based on a human rule-based procedure
(which is considered safe), gets the human labels, applies geometric data
augmentation, and retrains with the augmented data using transfer learning. We
evaluate the system on corrupted versions of the MNIST and FashionMNIST
datasets. We get an improvement in accuracy and trust score with just few
additional labels compared to a baseline approach.

15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat