Statistical mechanics used to explain machine learning and memorization

This thesis explores the theoretical underpinnings of machine learning and artificial neural networks using tools from statistical mechanics. It aims to improve understanding of how these systems learn and memorize data, focusing on implicitly low-dimensional learning structures and the theoretical basis of adversarial attacks. The research investigates dense associative memory and restricted Boltzmann machines to analyze different learning and memorization patterns. AI

IMPACT Provides a theoretical framework to better understand and potentially improve the robustness and learning capabilities of AI models.

RANK_REASON Academic paper on theoretical aspects of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• dense associative memory
• machine learning
• restricted Boltzmann machines
• statistical mechanics

• paper
• other

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