Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimalist Regression Network with Reinforced Gradients and Weighted
  Estimates: a Case Study on Parameters Estimation in Automated Welding   This paper presents a minimalist neural regression network as an aggregate of
independent identical regression blocks that are trained simultaneously.
Moreover, it introduces a new multiplicative parameter, shared by all the
neural units of a given layer, to maintain the quality of its gradients.
Furthermore, it increases its estimation accuracy via learning a weight factor
whose quantity captures the redundancy between the estimated and actual values
at each training iteration. We choose the estimation of the direct weld
parameters of different welding techniques to show a significant improvement in
calculation of these parameters by our model in contrast to state-of-the-arts
techniques in the literature. Furthermore, we demonstrate the ability of our
model to retain its performance when presented with combined data of different
welding techniques. This is a nontrivial result in attaining an scalable model
whose quality of estimation is independent of adopted welding techniques.

25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc