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Found 4 papers, 2 papers with code
Siamese networks for Poincaré embeddings and the reconstruction of evolutionary trees
We present a method for reconstructing evolutionary trees from high-dimensional data, with a specific application to bird song spectrograms.
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.
Intrinsic persistent homology via density-based metric learning
Our approach is based on the computation of persistent homology of the space of data points endowed with a sample metric known as Fermat distance.
Nonhomogeneous Euclidean first-passage percolation and distance learning
Consider an i. i. d.
Probability 60D05, 62G99
