Geometric entropy and phase transitions analyzed in continuous thermal dense associative memory

This paper explores the theoretical memory capacity of modern Hopfield networks, specifically Dense Associative Memory models with continuous states. It derives thermodynamic phase boundaries for these networks, comparing Gaussian and Epanechnikov kernels. The research indicates that geometric entropy is dependent on spherical geometry rather than the kernel, and it clarifies fundamental limits on retrieval robustness in attention-like memory architectures. AI

IMPACT Advances the theoretical understanding of high-capacity associative memory and its implications for modern attention-like architectures.

RANK_REASON This is a theoretical research paper published on arXiv detailing advancements in associative memory models. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Tatiana Petrova
• Dense Associative Memory
• Hopfield networks

• paper
• other

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