Sequential Learning in the Dense Associative Memory (2409.15729v2)

Abstract: Sequential learning involves learning tasks in a sequence, and proves challenging for most neural networks. Biological neural networks regularly conquer the sequential learning challenge and are even capable of transferring knowledge both forward and backwards between tasks. Artificial neural networks often totally fail to transfer performance between tasks, and regularly suffer from degraded performance or catastrophic forgetting on previous tasks. Models of associative memory have been used to investigate the discrepancy between biological and artificial neural networks due to their biological ties and inspirations, of which the Hopfield network is the most studied model. The Dense Associative Memory (DAM), or modern Hopfield network, generalizes the Hopfield network, allowing for greater capacities and prototype learning behaviors, while still retaining the associative memory structure. We give a substantial review of the sequential learning space with particular respect to the Hopfield network and associative memories. We perform foundational benchmarks of sequential learning in the DAM using various sequential learning techniques, and analyze the results of the sequential learning to demonstrate previously unseen transitions in the behavior of the DAM. This paper also discusses the departure from biological plausibility that may affect the utility of the DAM as a tool for studying biological neural networks. We present our findings, including the effectiveness of a range of state-of-the-art sequential learning methods when applied to the DAM, and use these methods to further the understanding of DAM properties and behaviors.

• The paper demonstrates that employing higher interaction vertices in DAM significantly improves sequential learning performance by enhancing memory capacity.
• The experiments on Permuted MNIST illustrate that both rehearsal-based and regularization approaches effectively mitigate catastrophic forgetting in DAM.
• The findings imply that optimizing DAM's architectural parameters can bridge gaps between traditional Hopfield networks and biologically plausible AI systems.

Sequential Learning in the Dense Associative Memory

The paper "Sequential Learning in the Dense Associative Memory" (2409.15729) investigates the performance of the Dense Associative Memory (DAM), a generalized form of the Hopfield network, in sequential learning tasks. The paper explores various existing sequential learning techniques and their efficacy when applied to this neural network model, which, although deviating from traditional biological models, offers unique insights into associative memory and potential implications for biologically plausible AI systems.

Introduction

Sequential learning, alternatively termed continual or lifelong learning, demands that models learn from tasks presented one after the other, building knowledge incrementally without the benefit of persistent exposure to past data. While biological neural networks adeptly perform sequential learning by applying knowledge from past tasks to acquired new skills more efficiently and avoiding catastrophic forgetting, artificial neural networks often substantially struggle in this setting. Existing strategies to improve sequential learning capabilities in artificial models involve architectural changes, regularization, and rehearsal-based methods.

The Hopfield network, a progenitor in associative memory, is valued for its simplicity and alignment with biological neural designs. However, it struggles with limited network capacity. To address these limitations, the Dense Associative Memory was introduced, offering exponentially greater capacities and interesting memory structures. The present paper focuses on applying sequential learning mechanisms to the DAM, thereby benchmarking its performance and examining how these approaches can mitigate catastrophic forgetting.

Dense Associative Memory and Related Concepts

The modern iteration of the Hopfield network, the Dense Associative Memory (DAM), introduces novel elements such as interaction vertices, which replace quadratic energy wells and allow for enhanced learning capabilities, albeit at the cost of biological plausibility. The transition from classical to Dense Memories highlights trade-offs like increased reliance on gradient descent over simpler Hebbian algorithms.

A significant feature of DAM is its varying interaction vertex, which influences capacity significantly. As interaction increases, memory capacity grows exponentially, proving useful for complex scenarios beyond sequential learning. The shift away from weight matrices to memory vectors, despite losing biological plausibility, opens avenues for enhanced memory performance while maintaining insights useful to biological learning models.

Experimental Design and Methodologies

The paper conducts experiments using the Permuted MNIST dataset to benchmark various sequential learning methods in DAM networks. Methods include architectural alterations, naive and pseudorehearsal, and regularization techniques like Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI).

Figure 1: Naive rehearsal hyperparameter search over rehearsal proportion, measuring the average accuracy on the validation data split. A rehearsal proportion of $0.0$ corresponds to vanilla learning, while $1.0$ corresponds to presenting all previous tasks alongside the new task. A higher average accuracy reflects better performance on sequential learning tasks.

Figure 2: Pseudorehearsal hyperparameter search over rehearsal proportion, examining average accuracy on the validation split.

The experimental design incorporates a consistent DAM configuration, evaluated on various methods to address catastrophic forgetting and improve sequential learning. Analysis of hyperparameters centers around tuning for optimal task performance as a reflection of distinct sequential learning abilities.

Results and Insights

The exhaustive analysis reveals that the DAM's interaction vertex strongly influences its sequential learning performance, with higher vertices often enhancing memory retention capabilities. Figure 1 and Figure 2 illustrate the marked improvement in performance through rehearsal methods and show how pseudorehearsal can lead to efficient memory preservation in networks with higher interaction vertices.

A critical observation is that weight regularization methods, including SI and memory-aware synapses, behave comparably to rehearsal-based techniques in improving average accuracy. However, GED and A-GED exhibit high volatility in results, as depicted by variations in interaction vertex influence and contrasting regularization dynamics.

Figure 3: L2 regularization hyperparameter search over regularization hyperparameter $\lambda$, measuring the average accuracy on the validation data split.

Examining data retention and interaction vertices, Table 1 underscores the importance of balancing regularization against learning stability. The balance of enhancement in accuracy across tuning parameter choices suggests potential areas for future research in optimizing DAM structures related to their biomedical parallels.

Implications and Conclusion

This paper provides a comprehensive assessment of DAMs in sequential learning, highlighting both the strengths in memory handling at increased interaction vertices and the persistent challenge of achieving plasticity without compromising stability. The findings suggest future investigation into optimizing interaction vertices and merging biologically plausible features could yield advancements in associative memory models.

The paper concludes with a reaffirmation of DAM's value in bridging traditional Hopfield networks and modern neural architectures. Further exploration into DAM's applications, particularly addressing neural plausibility and adaptability, could expand DAM's capabilities in new AI learning paradigms or integrated systems emulating human cognition.