Understanding The Effects of Incorporating Scientific Knowledge on Neural Network Outputs and Loss Landscapes

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dc.contributor.authorElhamod, Mohannaden
dc.contributor.committeechairKarpatne, Anujen
dc.contributor.committeememberHuang, Jia-Binen
dc.contributor.committeememberReddy, Chandan K.en
dc.contributor.committeememberRamakrishnan, Narendranen
dc.contributor.committeememberNorth, Christopher L.en
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2023-06-07T08:00:13Zen
dc.date.available2023-06-07T08:00:13Zen
dc.date.issued2023-06-06en
dc.description.abstractWhile machine learning (ML) methods have achieved considerable success on several mainstream problems in vision and language modeling, they are still challenged by their lack of interpretable decision-making that is consistent with scientific knowledge, limiting their applicability for scientific discovery applications. Recently, a new field of machine learning that infuses domain knowledge into data-driven ML approaches, termed Knowledge-Guided Machine Learning (KGML), has gained traction to address the challenges of traditional ML. Nonetheless, the inner workings of KGML models and algorithms are still not fully understood, and a better comprehension of its advantages and pitfalls over a suite of scientific applications is yet to be realized. In this thesis, I first tackle the task of understanding the role KGML plays at shaping the outputs of a neural network, including its latent space, and how such influence could be harnessed to achieve desirable properties, including robustness, generalizability beyond training data, and capturing knowledge priors that are of importance to experts. Second, I use and further develop loss landscape visualization tools to better understand ML model optimization at the network parameter level. Such an understanding has proven to be effective at evaluating and diagnosing different model architectures and loss functions in the field of KGML, with potential applications to a broad class of ML problems.en
dc.description.abstractgeneralMy research aims to address some of the major shortcomings of machine learning, namely its opaque decision-making process and the inadequate understanding of its inner workings when applied in scientific problems. In this thesis, I address some of these shortcomings by investigating the effect of supplementing the traditionally data-centric method with human knowledge. This includes developing visualization tools that make understanding such practice and further advancing it easier. Conducting this research is critical to achieving wider adoption of machine learning in scientific fields as it builds up the community's confidence not only in the accuracy of the framework's results, but also in its ability to provide satisfactory rationale.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:37530en
dc.identifier.urihttp://hdl.handle.net/10919/115351en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectKnowledge-Guided Machine Learningen
dc.subjectMachine Learning visualizationen
dc.subjectLoss landscape visualizationen
dc.titleUnderstanding The Effects of Incorporating Scientific Knowledge on Neural Network Outputs and Loss Landscapesen
dc.typeDissertationen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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