Transformers Enhanced with Dynamic Tanh for Improved Performance
By
kaycebasques
A good honest bake. Not flashy, but you'll finish the whole bagel.
Summary
This work introduces Dynamic Tanh (DyT) as a replacement for normalization layers in Transformers, showing that Transformers without normalization can achieve equal or better performance. DyT enables performance matching or exceeding normalized Transformers without extensive hyperparameter tuning, challenging the necessity of normalization layers in neural networks.
Key quotes
· 4 pulledNormalization layers are ubiquitous in modern neural networks and have long been considered essential.
By incorporating DyT, Transformers without normalization can match or exceed the performance of their normalized counterparts, mostly without hyperparameter tuning.
These findings challenge the conventional understanding that normalization layers are indispensable in modern neural networks.
DyT is inspired by the observation that layer normalization in Transformers often produces tanh-like, $S$-shaped input-output mappings.
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