Deep learning, a state-of-the-art machine learning approach, has shown outstanding
performance over traditional machine learning in identifying intricate structures in complex …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

Multilayer neural networks are among the most powerful models in machine learning, yet the
fundamental reasons for this success defy mathematical understanding. Learning a neural …

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic
resonance (MR) imaging–based attenuation correction (AC)(termed deep MRAC) in brain …

We present a general variational approach to determine the steady state of open quantum
lattice systems via a neural-network approach. The steady-state density matrix of the lattice …

Breast cancer is the most common cause of death for women worldwide. Thus, the ability of
artificial intelligence systems to detect possible breast cancer is very important. In this paper …

Neural networks promise to bring robust, quantitative analysis to medical fields. However,
their adoption is limited by the technicalities of training these networks and the required …

Despite tremendous success of the stochastic gradient descent (SGD) algorithm in deep
learning, little is known about how SGD finds generalizable solutions at flat minima of the …

Lacunes of presumed vascular origin (lacunes) are associated with an increased risk of
stroke, gait impairment, and dementia and are a primary imaging feature of the small vessel …

Estimation of a regression functions is a common goal of statistical learning. We propose a
novel nonparametric regression estimator that, in contrast to many existing methods, does …