Positional Encoding Exploration for Attention Models

This post introduces a novel approach to exploring positional encodings for attention mechanisms by leveraging group theory. It details the mathematical framework and its application in generating and analyzing different encoding spaces, aiming to improve the understanding and performance of attention-based models. This post extends that capability by exploring novel methods for sequence weighting, potentially leading to more efficient and accurate models.