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Found 4 papers, 3 papers with code
Differentiable Programming for Differential Equations: A Review
Many scientific models are based on differential equations, where differentiable programming plays a crucial role in calculating model sensitivities, inverting model parameters, and training hybrid models that combine differential equations with data-driven approaches.
Choosing the parameter of the Fermat distance: navigating geometry and noise
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve the results given by Euclidean distances by exploding the geometrical and statistical properties of the dataset.
Efficient adjustment sets in causal graphical models with hidden variables
Moreover, we show that if either no variables are hidden or if all the observable variables are ancestors of either treatment, outcome, or the variables that are used to decide treatment, a globally optimal adjustment set exists.
Nonhomogeneous Euclidean first-passage percolation and distance learning
Consider an i. i. d.
Probability 60D05, 62G99
