Deep kernel learning for integral measurements
arXiv:1909.01844v12 citations
Originality Synthesis-oriented
AI Analysis
This work addresses a specific challenge in computed tomography reconstruction, but it appears incremental as it adapts an existing method to a new data type.
The authors tackled the problem of applying deep kernel learning to line integral measurement data, demonstrating its feasibility on computed tomography reconstruction examples.
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.