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Full Description
This volume contains the proceedings of the 2024 Spring Central Sectional Meeting, held at the University of Wisconsin-Milwaukee, Milwaukee, WI, on April 20-21, 2024. Our motivation for this volume is to fill the void of mathematical literature in the current developments of artificial intelligence, machine learning, deep learning, geometric deep learning, geometric information theory, etc. While there are some excellent mathematical ideas in such developments, the literature is flooded by papers and books written by computer scientists and engineers that lightly touch upon these subjects, thereby missing deep mathematical understanding. This makes it difficult for anyone who wants to enter such areas of research. What is missing in the literature is exactly the theoretical mathematical background in artificial intelligence and machine learning.
Contents
Articles
Tony Shaska, Artificial neural networks on graded vector spaces
Mee Seong Im and Venkat R. Dasari, Computational complexity reduction of deep neural networks
Carl Henrik Ek, Oisin Kim and Challenger Mishra, Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm
Jose Luis Crespo, Jaime Gutierrez and Angel Valle, Neural network design options for RNG's verification
Mee Seong Im, Clement Kam and Caden Pici, Diagrammatics of information
Ilias Kotsireas and Tony Shaska, A neurosymbolic framework for geometric reduction of binary forms
Yuta Kambe, Yota Maeda and Tristan Vaccon, Geometric generality of Transformer-based Grobner basis computation
Mee Seong Im, Semi-invariants of filtered quiver representations with at most two pathways
Elira Curri and Tony Shaska, Polynomials, Galois groups, and database-driven arithmetic
