Kernel Hopfield networks show high storage capacity, stability limits analyzed

Researchers have analyzed the geometric properties and storage capacity limits of kernel Hopfield networks trained with Kernel Logistic Regression (KLR). Their experiments, using random sequences and CIFAR-10 image embeddings, indicate these networks can store up to approximately 16 random sequences per unit and maintain stable retrieval for structured data near a load of 20 sequences per unit. The study found that attractors are separated by sharp boundaries, and the ultimate storage limit is determined by dynamical stability against noise rather than geometric separability in the feature space. AI

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IMPACT Provides theoretical insights into the storage capacity and stability mechanisms of kernel Hopfield networks, potentially informing future memory system designs.

RANK_REASON This is a research paper published on arXiv detailing theoretical analysis and experimental findings on kernel Hopfield networks.

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• Kernel Logistic Regression
• Hopfield networks
• CIFAR-10
• Cover's theorem

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