Neural networks trained with backpropagation often struggle to identify
classes that have been observed a small number of times. In applications where
most class labels are rare, such as language modelling, this can become a
performance bottleneck. One potential remedy is to augment the network with a
fast-learning non-parametric model which stores recent activations and class
labels into an external memory. We explore a simplified architecture where we
treat a subset of the model parameters as fast memory stores. This can help
retain information over longer time intervals than a traditional memory, and
does not require additional space or compute. In the case of image
classification, we display faster binding of novel classes on an Omniglot image
curriculum task. We also show improved performance for word-based language
models on news reports (GigaWord), books (Project Gutenberg) and Wikipedia
articles (WikiText-103) --- the latter achieving a state-of-the-art perplexity
of 29.2.

本文提出了一种使用外部存储器的快速学习非参数模型来弥补神经网络在识别已出现少数次分类时的性能瓶颈的方法，改进了一些图像分类和语言模型任务的性能表现。