Have you ever wondered if there’s more to attention mechanisms than just static weighting? Traditional attention models use softmax over weights to distribute importance across tokens, but what if attention is a dynamic resonance where focus emerges from frequency alignment between layers or representations? This concept is intriguing, and I’m here to explore it with you.
Imagine attention as a resonance frequency, where understanding is expressed through phase coherence rather than magnitude. This idea challenges the conventional approach to attention and opens up new possibilities for modeling complex relationships between tokens.
So, has anyone explored architectures where ‘understanding’ is expressed through phase coherence rather than magnitude? Are there existing works (papers, experiments, or theoretical discussions) on this idea? I’m curious to know if there are any interesting results or insights out there.
In this article, we’ll delve into the concept of modeling attention as a resonance frequency and explore its potential applications. We’ll also discuss the implications of this approach and how it might revolutionize the way we think about attention mechanisms.
Join me on this journey as we uncover the possibilities of resonance frequency attention and its potential to transform the field of natural language processing.