Gruenbaum, Daniel (2022) Narrowing the gap between combinatorial and hyperbolic knot invariants via deep learning. JOURNAL OF KNOT THEORY AND ITS RAMIFICATIONS, 31 (01): 2250003. ISSN 0218-2165, 1793-6527
Full text not available from this repository. (Request a copy)Abstract
In this paper, we present a statistical approach for the discovery of relationships between mathematical entities that is based on linear regression and deep learning with fully connected artificial neural networks. The strategy is applied to computational knot data and empirical connections between combinatorial and hyperbolic knot invariants are revealed.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ; Deep learning; knot invariants; exploratory data analysis |
| Subjects: | 500 Science > 510 Mathematics |
| Divisions: | Mathematics |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Oct 2023 13:43 |
| Last Modified: | 17 Oct 2023 13:43 |
| URI: | https://pred.uni-regensburg.de/id/eprint/56495 |
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