22 citations · 28 across the 10 of their papers we have counts for
5 papers · 1 filter
Hierarchical Grading in Large Language Models
T. Shaska
We introduce Graded Large Language Models (GLLMs), an algebraic framework that equips the representation space of a transformer with a grading and propagates the induced weighted s…
Internalizing Tools as Morphisms in Graded Transformers
Tony Shaska
We introduce a graded formulation of internal symbolic computation for transformers. The hidden space is endowed with a grading , and symbolic operations a…
Neuro-Symbolic Learning for Galois Groups: Unveiling Probabilistic Trends in Polynomials
Elira Shaska, Tony Shaska
This paper presents a neurosymbolic approach to classifying Galois groups of polynomials, integrating classical Galois theory with machine learning to address challenges in algebra…
Graded Neural Networks
Tony Shaska
This paper presents a novel framework for graded neural networks (GNNs) built over graded vector spaces $\V_\w^n$, extending classical neural architectures by incorporating algebra…
Galois groups of polynomials and neurosymbolic networks
Elira Shaska, Tony Shaska
This paper introduces a novel approach to understanding Galois theory, one of the foundational areas of algebra, through the lens of machine learning. By analyzing polynomial equat…