paper-with-me

홈 › Papers

Learning proofs for the classification of nilpotent semigroups

2021-06-06 · Carlos Simpson

Machine learning is applied to find proofs, with smaller or smallest numbers of nodes, for the classification of 4-nilpotent semigroups.

📄 PDF Abstract BibTeX arXiv:2106.03015

Code (1)

carlostsimpson/sg-learn 공식 구현 pytorch

Tasks

BIG-bench Machine LearningClassification

Similar Papers 제목 키워드 기반

An algebraic characterization of self-generating chemical reaction networks using semigroup models

2022-07-12 · Dimitri Loutchko

The ability of a chemical reaction network to generate itself by catalyzed reactions from constantly present environmental food sources is considered a fundamental property in origin-of-life research. Based on Kaufmann's…

A Hybrid Framework for Healing Semigroups with Machine Learning

2025-09-01 · Sarayu Sirikonda, Jasper van de Kreeke arxiv

In this paper, we propose a hybrid framework that heals corrupted finite semigroups, combining deterministic repair strategies with Machine Learning using a Random Forest Classifier. Corruption in these tables breaks ass…

Time-optimal neural feedback control of nilpotent systems as a binary classification problem

2025-03-21 · Sara Bicego, Samuel Gue, Dante Kalise, Nelly Villamizar

A computational method for the synthesis of time-optimal feedback control laws for linear nilpotent systems is proposed. The method is based on the use of the bang-bang theorem, which leads to a characterization of the t…

Binary Classification

Embedding Knowledge Graphs in Degenerate Clifford Algebras

2024-02-06 · Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form $Cl_{p,q}$ (i.e., algebras without nilpotent base vectors) have b…

Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge Graphs

Algebraic Decomposition Theory for Transformer Length Generalization

2026-08-13 · Andy Yang, Blerta Veseli, Corentin Barloy, Michaël Cadilhac 외 arxiv

Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization. It is not even known w…