Saddle Hierarchy in Dense Associative Memory
This work addresses training efficiency and stability for dense associative memory models, which are relevant for robust machine learning applications like adversarial defense and attention mechanisms, but it is incremental as it builds on existing DAM frameworks.
The authors tackled the training instability of dense associative memory (DAM) models by deriving saddle-point equations from a statistical mechanics analysis, proposing a novel regularization scheme that significantly stabilizes training, and implementing a network-growing algorithm that reduces computational cost by leveraging a saddle-point hierarchy.
Dense associative memory (DAM) models have been attracting renewed attention since they were shown to be robust to adversarial examples and closely related to state-of-the-art machine learning paradigms, such as the attention mechanisms in transformers and generative diffusion models. We study a DAM built upon a three-layer Boltzmann machine with Potts hidden units, which represent data clusters and classes. Through a statistical mechanics analysis, we derive saddle-point equations that characterize both the stationary points of DAMs trained on real data and the fixed points of DAMs trained on synthetic data within a teacher-student framework. Based on these results, we propose a novel regularization scheme that makes training significantly more stable. Moreover, we show empirically that our DAM learns interpretable solutions to both supervised and unsupervised classification problems. Pushing our theoretical analysis further, we find that the weights learned by relatively small DAMs correspond to unstable saddle points in larger DAMs. We implement a network-growing algorithm that leverages this saddle-point hierarchy to drastically reduce the computational cost of training dense associative memory.