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This paper presents a minimalist neural regression network as an aggregate ofindependent identical regression blocks that are trained simultaneously.Moreover, it introduces a new multiplicative parameter, shared by all theneural units of a given layer, to maintain the quality of its gradients.Furthermore, it increases its estimation accuracy via learning a weight factorwhose quantity captures the redundancy between the estimated and actual valuesat each training iteration. We choose the estimation of the direct weldparameters of different welding techniques to show a significant improvement incalculation of these parameters by our model in contrast to state-of-the-artstechniques in the literature. Furthermore, we demonstrate the ability of ourmodel to retain its performance when presented with combined data of differentwelding techniques. This is a nontrivial result in attaining an scalable modelwhose quality of estimation is independent of adopted welding techniques.

Minimalist Regression Network with Reinforced Gradients and Weighted Estimates: a Case Study on Parameters Estimation in Automated Welding