Abstract Nowadays, Semi-Supervised Learning lies at the core of the Machine Learning field
trying to effectively exploit unlabeled data as much as possible, together with a small amount …

Multi-output regression aims at learning a map** from a multivariate input feature space to
a multivariate output space. Despite its potential usefulness, the standard formulation of the …

Dataset size continues to increase and data are being collected from numerous
applications. Because collecting labeled data is expensive and time consuming, the amount …

Contrastive learning methods can be applied to deep regression by enforcing label distance
relationships in feature space. However, these methods are limited to labeled data only …

Deep regression is an important problem with numerous applications. These range from
computer vision tasks such as age estimation from photographs, to medical tasks such as …

There are often the underlying cross relatedness amongst multiple tasks, which is discarded
directly by traditional single-task learning methods. Since multi-task learning can exploit …

Recently, support vector machine (SVM) has received much attention due to its good
performance and wide applicability. As a supervised learning algorithm, the standard SVM …

Given that citations are not equally important, various techniques have been presented to
identify important citations on the basis of supervised machine learning models. However …

Multi-output regression aims at map** a multivariate input feature space to a multivariate
output space. Currently, it is effective to extend the traditional support vector regression …

Twin neural network regression (TNNR) is trained to predict differences between the target
values of two different data points rather than the targets themselves. By ensembling …