Stochastic gradient descent is popular for large scale optimization but has slow convergence
asymptotically due to the inherent variance. To remedy this problem, we introduce an …

One of the most important issues in machine learning is whether one can improve the
performance of a supervised learning algorithm by including unlabeled data. Methods that use …

We consider the problem of minimizing the sum of two convex functions: one is the average
of a large number of smooth component functions, and the other is a general convex function …

Structural reliability concerns of various electromechanical devices call for a better
understanding of the mechanisms of fracture in piezoelectric ceramics subjected to combined …

The two-dimensional (2D) superconducting state is a fragile state of matter susceptible to
quantum phase fluctuations. Although superconductivity has been observed in ultrathin metal …

In this paper, we propose a novel deep learning framework, called spatial–temporal recurrent
neural network (STRNN), to integrate the feature learning from both spatial and temporal …

Spatial stratified heterogeneity, referring to the within-strata variance less than the between
strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many …

Convolution has been the core ingredient of modern neural networks, triggering the surge
of deep learning in vision. In this work, we rethink the inherent principles of standard …

Linear prediction methods, such as least squares for regression, logistic regression and support
vector machines for classification, have been extensively used in statistics and machine …

A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a
recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent …