Hopfield Networks Learn Graph Orbits: Implicit Bias and Invariance Examined

This ArXiv paper explores how Hopfield networks, traditionally used for associative memory, can efficiently learn graph orbits. The research likely contributes to a better understanding of how neural networks can represent and process graph-structured data, and may have implications for other machine learning tasks.

• •Hopfield networks are being applied to graph-structured data.
• •The research investigates implicit bias in the learning process.
• •The paper explores the concept of invariance within the network's function.