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Found 2 papers, 1 papers with code
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data
In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable.
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection
To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems.
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