Machine Learning Parameter Systems, Noether Normalisations and Quasi-stable Positions
Co-author(s): Amir Hashemi and Mahshid Mirhashemi
Reference: Preprint Isfahan and Kassel Universities 2023
Description: We study how different machine learning models may be used to put ideals into quasi-stable position (a generic position that shares many properties with the much harder to reach generic initial ideal - see [86] ). Based on a batch of 10.000 random ideals, we conclude that machine learning is much more efficient than human heuristics for this problem.
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