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Found 3 papers, 1 papers with code
Uncertainty quantification for learned ISTA
Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability.
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Newton method or a variable metric proximal gradient method applied to a non-convex rank surrogate.
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
The recovery of sparse data is at the core of many applications in machine learning and signal processing.
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