title: GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL
abstract: 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.
authors: Imanol Schlag Jürgen Schmidhuber
hIndex: Jürgen Schmidhuber 92