Preprint Article Version 1 This version is not peer-reviewed

Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing

Version 1 : Received: 30 September 2019 / Approved: 2 October 2019 / Online: 2 October 2019 (03:17:07 CEST)

A peer-reviewed article of this Preprint also exists.

Michelucci, U.; Venturini, F. Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing. Appl. Sci. 2019, 9, 4748. Michelucci, U.; Venturini, F. Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing. Appl. Sci. 2019, 9, 4748.

Journal reference: Appl. Sci. 2019, 9, 4748
DOI: 10.3390/app9224748

Abstract

The classical approach to non-linear regression in physics, is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then, using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterised by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built-in the models to compensate for the impossibility of modeling things that are, by construction, impossible to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here the MTL architecture allows predicting multiple parameters, the oxygen concentration and the temperature, from a single set of measurements.

Subject Areas

multi-task learning; non-linear regression; neural networks; luminescence; luminescence quenching; oxygen sensing; phase fluorimetry; temperature sensing

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