In recent years, unsupervised feature selection methods have raised considerable interest in
many research areas; this is mainly due to their ability to identify and select relevant features …

Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

Federated learning (FL) is a machine learning paradigm where a shared central model is
learned across distributed devices while the training data remains on these devices …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

In this paper, we address the challenging task of simultaneously optimizing (i) the weights of
a neural network,(ii) the number of neurons for each hidden layer, and (iii) the subset of …

Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …

Abstract Multi-task learning (MTL), which optimizes multiple related learning tasks at the
same time, has been widely used in various applications, including natural language …

Although deep learning approaches have had tremendous success in image, video and
audio processing, computer vision, and speech recognition, their applications to three …