Index Catalog // ScholarsArchive@OSU

Relational Learning over Dirty Data Using Data Constraints

Creator:: Lee, Ga Young
Abstract:: Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Applying machine learning on dirty databases may lead to inaccurate results. Users have to spend a lot of time and effort repairing data errors...
Resource Type:: Masters Thesis

Adversarial Attacks in Natural Language Question-Answering

Creator:: Rahurkar, Prachi S.
Abstract:: Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...
Resource Type:: Masters Thesis
Full Text:: Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In

Automated Monitoring of Construction Operations for Data-Driven Decision Making

Creator:: Rashid, Khandakar, M.
Abstract:: The continuous improvement of construction operations requires a systematic approach of monitoring and making appropriate control actions. However, the lack of real-time information hinders this workflow and eventually compromises timely and effective decision-making. Project managers spend a great deal of time and effort to solve problems emerging from lack of...
Resource Type:: Dissertation
Full Text:: memorable one. I am sincerely grateful to my doctorial committee members Dr. John Gambatese, Dr. Weng

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

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:: Koul, Anurag
Abstract:: Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
Resource Type:: Dissertation

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

Learning to coordinate in sparse asymmetric multiagent systems

Creator:: Dixit, Gaurav
Abstract:: Multiagent learning offers a rich framework to address challenging real-world problems such as remote exploration and healthcare coordination, which require autonomous agents to express elaborate interactions. To be effective in such systems, agents must collectively reason about and pursue high-level, long-term, and possibly nebulous objectives while adapting their strategy to...
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

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

Creator:: Ibrahim, Shahana
Abstract:: Crowdsourcing is a popular paradigm to address the high demands for labeled data in big data deluge. It aims to produce accurate labels by eﬀectively integrating noisy, non-expert labeling from crowdsourced workers (annotators). The machine learning community has been studying eﬀective crowdsourcing methods for many years, and many models and...
Resource Type:: Masters Thesis

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

Creator:: Khorram, Saeed
Abstract:: The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
Resource Type:: Dissertation

ScholarsArchive@OSU

Relational Learning over Dirty Data Using Data Constraints

Adversarial Attacks in Natural Language Question-Answering

Automated Monitoring of Construction Operations for Data-Driven Decision Making

Identifying Design Discussion Topics in Open Source Software

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

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

Learning to coordinate in sparse asymmetric multiagent systems

Efficient training and feature induction in sequential supervised learning

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

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