DeepMind & Toulouse U Contribute Composable Function Preserving Transformations to Boost Transformer Training

In a new paper Composable Function-preserving Expansions for Transformer Architectures, a research team from Google DeepMind and University of Toulouse introduces parameter expansion transformations for transformer-based neural networks while preserving functionality, enabling the expansion of the capability of the model as needed.

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