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...
The thesis contains all four chapters of my Ph.D. research on deep learning and text mining. The first chapter, "Temporal Topic Analysis with Endogenous and Exogenous Processes'', proposes a topic model which mines temporal economy-related documents with an exogenous economic indicator, and finds the relationship between document topics and the...
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...
A core problem in many computer vision applications is visual recognition (including object classification, detection and localization). Recent advances in artificial neural networks (aka ”deep learning”) have significantly pushed forward the state-of-the-art visual recognition performances. However, due to the lack of semantic structure modeling, most current deep learning approaches do...
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...
Recent developments in deep learning have led to breakthroughs in rendering novel views from sparse input views of a scene.While the accuracy of these algorithms has improved dramatically, it has come at a huge computational cost.
While developments in graphics hardware have ameliorated some of the computational burdens, deep learning-based...
Modeling human language is at the very frontier of machine learning and artificial intelligence. Statistical language models are probabilistic models that assign probabilities to sequences of words. For example, topic models are frequently used text-mining tools to organize a vast set of unstructured documents by exploring their theme structure. More...
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...
Commonsense inference is a critical capability of modern artificial intelligence (AI) systems. The machines need commonsense knowledge to perform tasks exactly like human being does. Learning commonsense inference from text has been a long standing challenge in the field of natural language processing due to reporting bias -- people do...