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Sejun Park
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Sejun Park11 visible / 11 total nodes / 20 links

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Co-authorshipCo-authorshipCo-authorshipAuthorshipAuthorshipAuthorshipAuthorshipWorks onWorks onWorks onAuthorshipAuthorshipAuthorshipAuthorshipAuthorshipTopic signalTopic signalTopic signalTopic signalTopic signalSejun ParkASejun ParkResearcherGeonho HwangAGeonho HwangResearcherYeachan ParkAYeachan ParkResearcherFloating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their GradientsWFloating-Point Networks...preprint / 2026Minimum Width for Universal ApproximationWMinimum Width for Unive...preprint / 2020Learning with End-Users in Distribution Grids: Topology and Parameter EstimationWLearning with End-Users...preprint / 2020Lookahead: A Far-Sighted Alternative of Magnitude-based PruningWLookahead: A Far-Sighte...preprint / 2020Machine LearningTMachine Learning49008 worksLearning Bounds for Risk-sensitive LearningWLearning Bounds for Ris...preprint / 2021Electron spin relaxations of phosphorus donors in bulk silicon under large electric fieldWElectron spin relaxatio...preprint / 2020Minimum Weight Perfect Matching via Blossom Belief PropagationWMinimum Weight Perfect ...preprint / 2015
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Sejun Park

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PersonGeonho HwangCo-authorship / ResearcherPersonYeachan ParkCo-authorship / ResearcherPaperFloating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their GradientsAuthorship / preprint / 2026PaperMinimum Width for Universal ApproximationAuthorship / preprint / 2020PaperLearning with End-Users in Distribution Grids: Topology and Parameter EstimationAuthorship / preprint / 2020PaperLookahead: A Far-Sighted Alternative of Magnitude-based PruningAuthorship / preprint / 2020TopicMachine LearningWorks on / 49008 worksPaperLearning Bounds for Risk-sensitive LearningAuthorship / preprint / 2021

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