Christopher Hillar: Hopfield Networks for Graph Isomorphism
Algebraic
[Bernstein Seminar]
| When |
Jun 17, 2026
from 12:15 AM to 01:00 PM |
|---|---|
| Where | NEXUS Lab, IMBIT, Technische Fakultät |
| Contact Name | Martina Bacher |
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Abstract
Discrete recurrent neural networks have a long history, starting with McCulloch and Pitt's seminal paper in 1943 that ushered in the computer era as well as inspired the modern field of deep learning. We discuss some of the mathematics underlying the special case of Hopfield networks, which have received considerable attention in recent years (Hopfield won the Nobel prize). Specifically, we show how these networks can robustly store an exponential number of memories, originally thought to be impossible using only linear-threshold computational primitives. Applications to error-correcting codes, generalization in AI, and graph isomorphism will be discussed.