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Parametric Matrix Models
We present a general class of machine learning algorithms called parametric matrix models.
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Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Unsupervised Learning for Identifying Events in Active Target Experiments
We also explore the application of clustering the latent space of autoencoder neural networks for event separation.
Integrating a Computational Perspective in Physics Courses
In this contribution we discuss how to develop a physics curriculum for undergraduate students that includes computing as a central element.
Physics Education Computational Physics
