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LearningfromStructuredDatawith
WeakSupervision
HanchenWang
Supervisor:JoanLasenby
DepartmentofEngineering
UniversityofCambridge
Thisdissertationissubmittedforthedegreeof
DoctorofPhilosophy
February2023
Declaration
Iherebydeclarethatexceptwherespecificreferenceismadetotheworkofothers,the
contentsofthisdissertationareoriginalandhavenotbeensubmittedinwholeorinpart
forconsiderationforanyotherdegreeorqualificationinthis,oranyotheruniversity.This
dissertationismyownworkandcontainsnothingwhichistheoutcomeofworkdonein
collaborationwithothers,exceptasspecifiedinthetextandAcknowledgements.This
dissertationcontainsfewerthan65,000words,includingappendices,bibliography,footnotes,
tablesandequationsandhasfewerthan150figures.
HanchenWang
February2023
Abstract
Inallscience,inquiryproceedsbasedonobservationandexperimentation,exercisinginformed
judgementanddevelopinghypothesestoguidethedesignofexperimentsanddisambiguate
thetheories.Artificialintelligence(AI)hasdramaticallyimprovedstate-of-the-artscientific
researchbyhelpingscientiststoformulatehypotheses,designexperimentstotestthem,and
collectandinterpretdata.Fundamentaladvancesoverthepastdecadeincludeself-supervised
learningmethodsthattrainmodelsonbroaddataatscalewithoutpre-definedlabels,geometric
deeplearningthatleveragesstructureandgeometryinformedbyscientificknowledge,and
generativeAImethodstha
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