Deep kernel learning for integral measurements
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Abstract (arXiv)
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feasible for problems where the data consists of line integral measurements of the target function. The performance is illustrated on computed tomography reconstruction examples.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Sep 2019.
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Citing papers per year: 2021: 2.
2 papers.