AI RESEARCH
Parameter Space Analysis through Guided Visual Interpolations
arXiv CS.LG
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ArXi:2509.19202v2 Announce Type: replace-cross We propose Parameter Space Analysis through Guided Visual Interpolations (ParamInter), a novel tool for high-dimensional input parameter space analysis by making interpolation towards optimal parameter sets explorable using guided analytics. The interpolation is accompanied by both small multiples in linked views and utilizes t-Distributed Stochastic Neighbor Embedding (t-SNE) representations to show an interpolation overview.