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ESE Spring Seminar – “Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models”
ESE Spring Seminar – “Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models”
: Generative priors are effective countermeasures to combat the curse of dimensionality, and enable efficient learning and inversion that otherwise are ill-posed, in data science. This talk begins with the classical low-rank prior, and introduces scaled gradient descent (ScaledGD), a simple iterative approach to directly recover the low-rank factors for a wide range of matrix […]