| Softcover ISBN: | 978-1-4704-7686-1 |
| Product Code: | CONM/835 |
| List Price: | $135.00 |
| MAA Member Price: | $121.50 |
| AMS Member Price: | $108.00 |
| eBook ISBN: | 978-1-4704-8527-6 |
| Product Code: | CONM/835.E |
| List Price: | $129.00 |
| MAA Member Price: | $116.10 |
| AMS Member Price: | $103.20 |
| Softcover ISBN: | 978-1-4704-7686-1 |
| eBook: ISBN: | 978-1-4704-8527-6 |
| Product Code: | CONM/835.B |
| List Price: | $264.00 $199.50 |
| MAA Member Price: | $237.60 $179.55 |
| AMS Member Price: | $211.20 $159.60 |
| Softcover ISBN: | 978-1-4704-7686-1 |
| Product Code: | CONM/835 |
| List Price: | $135.00 |
| MAA Member Price: | $121.50 |
| AMS Member Price: | $108.00 |
| eBook ISBN: | 978-1-4704-8527-6 |
| Product Code: | CONM/835.E |
| List Price: | $129.00 |
| MAA Member Price: | $116.10 |
| AMS Member Price: | $103.20 |
| Softcover ISBN: | 978-1-4704-7686-1 |
| eBook ISBN: | 978-1-4704-8527-6 |
| Product Code: | CONM/835.B |
| List Price: | $264.00 $199.50 |
| MAA Member Price: | $237.60 $179.55 |
| AMS Member Price: | $211.20 $159.60 |
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Book DetailsContemporary MathematicsVolume: 835; 2026; 294 ppMSC: Primary 15; 62; 14; 32; 68; 94; 11
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.
ReadershipGraduate students and research mathematicians interested in artificial intelligence and mathematics research.
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Table of Contents
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Articles
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Tony Shaska — Artificial neural networks on graded vector spaces
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Mee Seong Im and Venkat R. Dasari — Computational complexity reduction of deep neural networks
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Carl Henrik Ek, Oisin Kim and Challenger Mishra — Calabi–Yau metrics through Grassmannian learning and Donaldson’s algorithm
-
José Luis Crespo, Jaime Gutierrez and Angel Valle — Neural network design options for RNG’s verification
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Mee Seong Im, Clement Kam and Caden Pici — Diagrammatics of information
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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 Gröbner 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
-
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RequestsReview Copy – for publishers of book reviewsAccessibility – to request an alternate format of an AMS title
- Book Details
- Table of Contents
- Requests
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.
Graduate students and research mathematicians interested in artificial intelligence and mathematics research.
-
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
-
José 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 Gröbner 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
