噪声样例上的注意力：指数 Hopfield 网络的泛化与记忆

arXiv:2610.22191v2 Announce Type: replace-cross Abstract: In Hebbian learning from examples, a Hopfield network retrieves archetypes seen only through noisy examples. We study it in the exponential Hopfield model: one softmax attention step with the unlabelled examples as keys. A signal-to-noise analysis gives the overlap with an unseen archetype in closed form: a majority-rule law in which an attention entropy replaces the dataset entropy. At high temperature the archetype is lost in the crosstalk; at low temperature the softmax freezes on the closest training example, as in a random energy model, and the layer memorizes. In between, in a window widening with the square root of the network size, the layer pools the examples of the correct class and retrieves the unseen archetype as well as their majority vote allows, at any fixed load. On structured datasets, such as MNIST and fashion-MNIST, the best accuracy lies beyond the freezing.

中文翻译还没有生成，先显示原文。