✳flâneur — a map of the web's best reading

Hydra Part I - Matrix Mixer Framework | Goomba Lab

goombalab.github.io · 2,082 words · saved by 1 readers

Attention mechanismshave taken center stage in the world of sequence mixing, celebrated for their significant flexibility and performance. However, this power comes with a price: high computational and memory demands. Despite these challenges, attention has become the go-to solution for many applications. In modern state-of-the-art models, architectural designs typically split into two main components: the sequence mixer and the channel mixer. To illustrate, let’s look at the Transformer encoder architecture. It consists of two key elements: Multi-Head Attention and a Feed-Forward Network (FFN). The Multi-Head Attention serves as the sequence mixer, efficiently managing interactions across the input sequence. Meanwhile, the FFN acts as the channel mixer, processing information within each sequence element. Take a glance at the figure below to see this architecture in action. You’ll notice how these components work together to create the robust models we rely on today. In our work, we s

Hydra Part I - Matrix Mixer Framework | Goomba Lab Hydra Part I - Matrix Mixer Framework [ Paper ] [ Code ] Part I - Matrix Mixer Framework Part II - Hydra: The Model Attention mechanisms In this work, Attention exclusively refers to Self-Attention have taken center stage in the world of sequence mixing, celebrated for their significant flexibility and performance. However, this power comes with a price: high computational and memory demands. Despite these challenges, attention has become the go-to solution for many applications. In modern state-of-the-art models, architectural designs typical

Explore this link on the map →

related reading