Search Results for author: Pablo Groisman

Found 4 papers, 2 papers with code

Siamese networks for Poincaré embeddings and the reconstruction of evolutionary trees

no code implementations9 Oct 2024 Ciro Carvallo, Hernán Bocaccio, Gabriel B. Mindlin, Pablo Groisman

We present a method for reconstructing evolutionary trees from high-dimensional data, with a specific application to bird song spectrograms.

Dimensionality Reduction

Choosing the parameter of the Fermat distance: navigating geometry and noise

no code implementations30 Nov 2023 Frédéric Chazal, Laure Ferraris, Pablo Groisman, Matthieu Jonckheere, Frédéric Pascal, Facundo Sapienza

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.

Navigate

Intrinsic persistent homology via density-based metric learning

1 code implementation11 Dec 2020 Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman

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.

Anomaly Detection Metric Learning +2

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