  Training a deep neural network requires a large amount of single-task data
and involves a long time-consuming optimization phase. This is not scalable to
complex, realistic environments with new unexpected changes. Humans can perform
fast incremental learning on the fly and memory systems in the brain play a
critical role. We introduce Sparse Meta Networks -- a meta-learning approach to
learn online sequential adaptation algorithms for deep neural networks, by
using deep neural networks. We augment a deep neural network with a
layer-specific fast-weight memory. The fast-weights are generated sparsely at
each time step and accumulated incrementally through time providing a useful
inductive bias for online continual adaptation. We demonstrate strong
performance on a variety of sequential adaptation scenarios, from a simple
online reinforcement learning to a large scale adaptive language modelling.
