Deep learning has been widely applied and brought breakthroughs in speech recognition,
computer vision, and many other domains. Deep neural network architectures and …

Establishing a solid theoretical foundation for structured deep neural networks is greatly
desired due to the successful applications of deep learning in various practical domains …

We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …

Deep learning based on structured deep neural networks has provided powerful
applications in various fields. The structures imposed on the deep neural networks are …

A typical challenge for construction of accurate soft sensors in the process industries is that
industrial process data often contains various noise and outliers. Inspired by correntropy in …

Deep learning based on deep neural networks of various structures and architectures has
been powerful in many practical applications, but it lacks enough theoretical verifications. In …

Convolutional neural networks (CNNs) have gained immense popularity in recent years,
finding their utility in diverse fields such as image recognition, natural language processing …

Recently, there is a growing interest in studying pairwise learning since it includes many
important machine learning tasks as specific examples, eg, metric learning, AUC …

We consider a family of deep neural networks consisting of two groups of convolutional
layers, a downsampling operator, and a fully connected layer. The network structure …

Within the statistical learning framework, this paper studies the regression model associated
with the correntropy induced losses. The correntropy, as a similarity measure, has been …