Recent advances in
mathematics and artificial intelligence

Editors
Alessandro Arsie, Elira Curri, Mee Seong Im, Tony Shaska

Contemporary Mathematics, 2024, American Mathematical Society

Submissions

If you are an author interested to contribute paper, please fill the following form

Motivation

Advances in artificial intelligence, machine learning, and data analysis in the last decade have made it possible to develop more sophisticated AI systems which are being used in a broad range of applications. However, the mathematical background for much of these new developments does not exist or is poorly written. As part of our efforts to get the mathematical research community more involved in Machine Learning and Artificial Intelligence in general, we are organizing a special session in the AMS Sectional Meeting in Milwaukee, titled “Artificial Intelligence and Mathematics”.

The special session will focus on applications of Artificial Intelligence to mathematics, especially on ideas which are based on concrete, explicit computational results through implementation and use of AI packages and methods. Moreover, we explore new mathematical ideas to construct better neural networks.

This proceedings volume as part of the series Contemporary Mathematics will be based on the work presented in this special session. We also intend to invite other researchers and mathematicians who will not be able to attend the sectional meeting.

Topics of this volume will be:

Expositions and Surveys on, but are not limited to:

A mathematical introduction to Transformers and NLP
Weight-space symmetry in deep networks
Equivariant Architectures for Learning in Deep Weight Spaces
Topological Deep Learning
Convolutional Neural Networks
Connections between neural networks and invariant theory
Geometric Deep Learning

Applications of Artificial Intelligence and Mathematics:

Machine learning and automorphism groups of curves
Using ML for superelliptic families of curves of high genus
Arithmetic in the moduli space: an ML approach
Data analysis of Calabi-Yau hypersurfaces via weighted heights
Machine learning and isogenies of abelian varieties of dimension 2 and 3
Machine learning and toric varieties
Machine learning and cryptography
Support vector machines

We expect each submitted paper to have between 20-30 pages.

Referring

All papers will be refereed by at least two independent referees following normal procedures of refereeing in mathematical journals.

Timeline:

Papers due: September 15, 2024
Reviewing Process: September 15 - December 15, 2024
Volume ready for the Publisher: January 1, 2025

Contact: Elira Shaska (elirashaska[AT]oakland.edu)

Papers received

Growing an architecture for a neural network, S. Khashin and E. Shemyakova
A machine learning approach of Julia reduction, I. Kostireas
Determining Galois groups of polynomials using machine learning
Artificial neural networks on graded manifolds,
Geometry of Weighted Projective Space and Machine Learning, S. Salami, T. Shaska
Homomorphic Encryption and Machine Learning, E. Curri