Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"
Image for the paper "Polytopes and Machine Learning"

Machine learning polytopes

A supervised machine of learning lattice polytopes predicts properties of volume, dual volume, and reflexivity with up to 100% accuracy.

Polytopes and Machine Learning

Arxiv (2023)

J. Bao, Y. He, E. Hirst, J. Hofscheier, A. Kasprzyk, S. Majumder

We introduce machine learning methodology to the study of lattice polytopes. With supervised learning techniques, we predict standard properties such as volume, dual volume, reflexivity, etc, with accuracies up to 100%. We focus on 2d polygons and 3d polytopes with Plücker coordinates as input, which out-perform the usual vertex representation.

Arxiv (2023)

J. Bao, Y. He, E. Hirst, J. Hofscheier, A. Kasprzyk, S. Majumder