Associative memory architectures are designed for memorization but also
offer, through their retrieval method, a form of generalization to unseen
inputs: stored memories can be seen as prototypes from this point of view.
Focusing on Modern Hopfield Networks (MHN), we show that a large memorization
capacity undermines the generalization opportunity. We offer a solution to
better optimize this tradeoff. It relies on Minimum Description Length (MDL) to
determine during training which memories to store, as well as how many of them.

关于现代霍普菲尔德网络（MHN），我们展示了大容量的记忆会削弱泛化机会，并提供了一种优化这种权衡的解决方案。该解决方案依赖于最小描述长度（MDL），在训练过程中确定存储哪些记忆以及存储多少记忆。

最小描述长度 Hopfield 网络

Minimum Description Length Hopfield Networks

We present two methods for unsupervised segmentation of words into
morpheme-like units. The model utilized is especially suited for languages with
a rich morphology, such as Finnish. The first method is based on the Minimum
Description Length (MDL) principle and works online. In the second method,
Maximum Likelihood (ML) optimization is used. The quality of the segmentations
is measured using an evaluation method that compares the segmentations produced
to an existing morphological analysis. Experiments on both Finnish and English
corpora show that the presented methods perform well compared to a current
state-of-the-art system.

本研究提出了两种方法来对单词进行无监督分词，特别适用于富有形态学的语言，如芬兰语。第一种方法基于 MDL 原则并且可以在线进行。在第二种方法中使用了最大似然 (ML) 优化。比较所得到的分割和现有的形态分析，证明这些方法在芬兰语和英语语料库上表现良好，相对于现有的一流系统而言。