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Towards a Theoretical Understanding of Inverse Problems with Neural Priors

Inverse problems of various flavors span all of science, engineering, and design. Over the last five years, approaches based on neural networks have emerged as the tool of choice for solving such problems. However, a clear theoretical understanding of how well such approaches perform — together with quantitative sample-complexity and …