Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces.

Last update: Sep 08, 2022

Overview

Weighted Projective Spaces ML

Description:
The database of 5-vectors describing 4d weighted projective spaces which admit Calabi-Yau hypersurfaces are examined with supervised and unsupervised ML techniques.

How to run:
~ Datasets available in the folder 'Data', including the h11 clustering results.
~ Scripts related to investigations are labelled accordingly. They are run cell-by-cell sequentially with commented points to change parameters for analysis. ML is run with the use of sci-kit learn.

Owner

Ed Hirst

PhD Student at City, University of London

GitHub Repository

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Supervised & unsupervised machine-learning techniques are applied to the database of weighted P4s which admit Calabi-Yau hypersurfaces.

Related tags

Overview

Weighted Projective Spaces ML

Owner

Ed Hirst

Code, pre-trained models and saliency results for the paper "Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images".

Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

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