In 2009, the Health Information Technology for Economic and Clinical Health Act (HITECH) promoted national use of electronic health records (EHR) in the US by giving incentives to providers who adopt ‘meaningful use’ of EHRs. As of 2017, nearly 86% of office-based physicians had adopted EHRs. EHRs have rich information...
The task of classification has been increasingly attracting attention from researchers in recent years. The objective is to assign labels given attributes of samples. The classification task is practical in real-world applications and is widely explored in fields such as computer vision, natural language processing and information retrieval. The recent...
Deep learning is a new area of machine learning research that allows deep neural networks composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning has helped in achieving the objective of pushing machine learning closer to one of its original goals of...
The ability of a machine to synthesize textual output in a form of human language is a long-standing goal in a field of artificial intelligence and has wide-range of applications such as spell correction, speech recognition, machine translation, abstractive summarization, etc. The statistical approach to enable such ability mainly involves...
In this dissertation, we start with the dictionary learning (DL) based single-frame super-resolution (SR) problem, where low resolution (LR) input frames are super-resolved to high resolution (HR) output frames. We propose to extend the previous single-frame SR methods to multiple-frames, i.e., estimating single HR output frame by multiple LR input...
The goal of this thesis is to design practical algorithms for nonlinear optimization in the case when the objective function is stochastic or nonsmooth. The thesis is divided into three chapters. Chapter 1 describes an active-set method for the minimization of an objective function that is structurally nonsmooth, viz., it...