Transfer learning aims to integrate useful information from multi-source datasets to improve
the learning performance of target data. This can be effectively applied in genomics when …

The inference of physical parameters from measured data is essential for describing and
analysing several complex systems. In this regard, the inverse problem theory has been …

The conventional approach to data-driven inversion framework is based on Gaussian
statistics that presents serious difficulties, especially in the presence of outliers in the …

The problem of inferencing parameters of complex systems from measured data has been
extensively studied based on the inverse problem theory. Classically, an inverse problem is …

Data-driven inferential sensor has been widely adopted to estimate key quality relevant
variables. However, industrial dataset usually presents many characteristics such as …

In this work we propose a robust methodology to mitigate the undesirable effects caused by
outliers to generate reliable physical models. In this way, we formulate the inverse problems …

Owing to the requirements of various product grades or operation conditions, most of
industrial processes work with multiple modes. Gaussian mixture regression (GMR) is one of …

The problem of obtaining physical parameters that cannot be directly measured from
observed data arises in several scientific fields. In the classic approach, the well-known …

The seismic data inversion from observations contaminated by spurious measures (outliers)
remains a significant challenge for the industrial and scientific communities. This difficulty is …

The estimation of physical parameters from data analyses is a crucial process for the
description and modeling of many complex systems. Based on Rényi α-Gaussian …