  We augment recurrent neural networks with an external memory mechanism that
builds upon recent progress in metalearning. We conceptualize this memory as a
rapidly adaptable function that we parameterize as a deep neural network.
Reading from the neural memory function amounts to pushing an input (the key
vector) through the function to produce an output (the value vector). Writing
to memory means changing the function; specifically, updating the parameters of
the neural network to encode desired information. We leverage training and
algorithmic techniques from metalearning to update the neural memory function
in one shot. The proposed memory-augmented model achieves strong performance on
a variety of learning problems, from supervised question answering to
reinforcement learning.
