  Standard neural network architectures are non-linear only by virtue of a
simple element-wise activation function, making them both brittle and
excessively large. In this paper, we consider methods for making the
feed-forward layer more flexible while preserving its basic structure. We
develop simple drop-in replacements that learn to adapt their parameterization
conditional on the input, thereby increasing statistical efficiency
significantly. We present an adaptive LSTM that advances the state of the art
for the Penn Treebank and WikiText-2 word-modeling tasks while using fewer
parameters and converging in less than half as many iterations.
