Visual Exploration of Neural Network Projection Stability

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dc.contributor.authorBredius, Carloen_US
dc.contributor.authorTian, Zonglinen_US
dc.contributor.authorTelea, Alexandruen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorNabney, Ianen_US
dc.contributor.editorPeltonen, Jaakkoen_US
dc.date.accessioned2022-06-02T09:48:25Z
dc.date.available2022-06-02T09:48:25Z
dc.date.issued2022
dc.description.abstractWe present a method to visually assess the stability of deep learned projections. For this, we perturb the high-dimensional data by controlled sequences and visualize the resulting changes in the 2D projection. We apply our method to a recent deep learned projection framework on several training configurations (learned projections and real-world datasets). Our method, which is simple to implement, runs at interactive rates, sheds several novel insights on the stability of the explored method.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20221068
dc.identifier.isbn978-3-03868-182-3
dc.identifier.pages1-5
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/mlvis.20221068
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mlvis20221068
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Information visualization; Visual analytics; Visualization systems and tools
dc.subjectHuman centered computing
dc.subjectInformation visualization
dc.subjectVisual analytics
dc.subjectVisualization systems and tools
dc.titleVisual Exploration of Neural Network Projection Stabilityen_US
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Machine Learning Methods in Visualisation for Big Data 2022