Papers › Breaking the Activation Function Bottleneck through Adaptive Parameterization

Breaking the Activation Function Bottleneck through Adaptive Parameterization

22 May 2018NeurIPS 2018 12arXiv:1805.08574archive 2025-07-28

Sebastian Flennerhag, Hujun Yin, John Keane, Mark Elliot

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.

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