用随机特征重构高容量记忆网络，固定参数量即可新增记忆。
Dense Associative Memory Through the Lens of Random Features
- 用随机特征替代传统权重存储，实现参数量不变的扩展
- 新记忆可修改已有权重添加，不增加网络规模
- 保持原模型能量函数与计算特性，适合大规模记忆系统
密集关联记忆是高存储容量的霍普菲尔德网络变体，可在给定网络规模下存储大量记忆模式。传统方法需为每条模式分配独立突触权重，导致新增记忆时权重数量增加。本文提出基于随机特征的新构造方式，使网络参数数量保持固定；同时可通过调整已有权重来添加新记忆。实验表明，该新型网络能近似传统密集关联记忆的能量函数和动态行为，并保留其优良计算特性。
原文摘要 · Abstract (English)
Dense Associative Memories are high storage capacity variants of the Hopfield networks that are capable of storing a large number of memory patterns in the weights of the network of a given size. Their common formulations typically require storing each pattern in a separate set of synaptic weights, which leads to the increase of the number of synaptic weights when new patterns are introduced. In this work we propose an alternative formulation of this class of models using random features, commonly used in kernel methods. In this formulation the number of network's parameters remains fixed. At the same time, new memories can be added to the network by modifying existing weights. We show that this novel network closely approximates the energy function and dynamics of conventional Dense Associative Memories and shares their desirable computational properties.
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