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London Institute of Mathematical Sciences
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Geometry-guided width allocation in Transformers can lead to substantial improvements in model performance, reducing validation loss more effectively than traditional methods.
Retaining only the tangential component of the feed-forward network can preserve model quality while enhancing prediction accuracy in Transformer dynamics.
ARMT-augmented models can handle inputs far beyond their original context limits while using 30% less compute, revolutionizing efficiency in long-context processing.
