Sebastian Flennerhag
Breaking the Activation Function Bottleneck through Adaptive Parameterization
Sebastian Flennerhag, Hujun Yin, John Keane, Mark Elliot
Breaking the Activation Function Bottleneck through Adaptive Parameterization
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.