  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.
