Breaking The Activation Function Bottleneck Through Adaptive Parameterization

Authors:
Sebastian Flennerhag Alan Turing Institute
Hujun Yin University of Manchester
John Keane University of Manchester
Mark Elliot University of Manchester

Introduction:

Standard neural network architectures are non-linear only by virtue of a simple element-wise activation function, making them both brittle and excessively large.The authors 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 half as many iterations.

Abstract:

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 half as many iterations.

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