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  1. Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperpa- rameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter values is computationally intensive and unintuitive due to the stochastic nature of such methods. In this paper we propose Hy- perNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. A HyperNP model can be trained on a fraction of the total data instances and hyperparameter configurations that one would like to investigate and can computemore »projections for new data and hyperparameters at interactive speeds. HyperNP models are compact in size and fast to compute, thus allowing them to be embedded in lightweight visualiza- tion systems. We evaluate the performance of HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP models are accurate, scalable, interactive, and appropriate for use in real-world settings.« less
    Free, publicly-accessible full text available June 13, 2023
  2. Gresalfi, M. ; Horn, I. S. (Ed.)
    There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the fieldmore »of STEM and CT integration.« less
  3. Gresalfi, M. ; Horn, I. S. (Ed.)
    There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the fieldmore »of STEM and CT integration.« less
  4. G. Gunzelmann, A. Howes (Ed.)