Index Catalog // ScholarsArchive@OSU

Identifying Design Discussion Topics in Open Source Software

Creator:: Chatterjee, Amreeta
Abstract:: When contributing to a software system, developers need to understand the rationale for previous design decisions so that they can adhere to the system’s design. Not doing so can lead to erosion of the overall design quality of the system. However, discussions embedded in a large volume of communication on...
Resource Type:: Masters Thesis

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Creator:: Gómez Fernández, Mario Enrique
Abstract:: Known as the fourth industrial revolution, digitization is an ongoing trend in all ﬁelds, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and...
Resource Type:: Dissertation

Probabilistic models for quality control in environmental sensor networks

Creator:: Dereszynski, Ethan W.
Abstract:: Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
Resource Type:: Dissertation

Interpretable Machine Learning: Applications in Biology and Genomics

Creator:: Danaee, Padideh
Abstract:: Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
Resource Type:: Dissertation

Integrating learning and search for structured prediction

Creator:: Doppa, Janardhan Rao
Abstract:: We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
Resource Type:: Dissertation
Full Text:: inference (Murphy et al., 1999; Andrieu et al., 1999). 8 While such methods have shown some success in

A study of methods for fine-grained object classification of arthropod specimens

Creator:: Lin, Junyuan
Abstract:: Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
Resource Type:: Masters Thesis

Bayesian optimization with empirical constraints

Creator:: Azimi, Javad
Abstract:: Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
Resource Type:: Dissertation

Efficient training and feature induction in sequential supervised learning

Creator:: Hao, Guohua
Abstract:: Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
Resource Type:: Dissertation

Effective decision-theoretic assistance through relational hierarchical models

Creator:: Natarajan, Sriraam
Abstract:: Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...
Resource Type:: Dissertation

ScholarsArchive@OSU

Identifying Design Discussion Topics in Open Source Software

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Probabilistic models for quality control in environmental sensor networks

Interpretable Machine Learning: Applications in Biology and Genomics

Integrating learning and search for structured prediction

A study of methods for fine-grained object classification of arthropod specimens

Bayesian optimization with empirical constraints

Efficient training and feature induction in sequential supervised learning

Effective decision-theoretic assistance through relational hierarchical models

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