Closed-form rank-constrained linear estimators, derived from Bayes risk, unify forward modeling, inverse recovery, autoencoding, and denoising, and often match or beat trained neural networks.
Surrogate and reduced-order modeling: a comparison of approaches for large- scale statistical inverse problems
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Optimal Linear Baseline Models for Scientific Machine Learning
Closed-form rank-constrained linear estimators, derived from Bayes risk, unify forward modeling, inverse recovery, autoencoding, and denoising, and often match or beat trained neural networks.