We improve previous end-to-end differentiable neural networks (NNs) with fast
 weight memories. A gate mechanism updates fast weights at every time step of
 a sequence through two separate outer-product-based matrices generated by slow
 parts of the net. The system is trained on a complex sequence to sequence variation
 of the Associative Retrieval Problem with roughly 70 times more temporal
 memory (i.e. time-varying variables) than similar-sized standard recurrent NNs
 (RNNs). In terms of accuracy and number of parameters, our architecture outperforms
 a variety of RNNs, including Long Short-Term Memory, Hypernetworks,
 and related fast weight architectures.
An improved Fast Weight network which shows better results on a general toy task.