Scalable Adaptive Computation for Iterative Generation

Understand

Natural data is redundant yet predominant architectures tile computation uniformly across their input and output space.

• We propose the Recurrent Interface Networks (RINs), an attention-based architecture that decouples its core computation from the dimensionality of the data, enabling adaptive computation for more scalable generation of high-dimensional data.
• RINs focus the bulk of computation (i.e.
• global self-attention) on a set of latent tokens, using cross-attention to read and write (i.e.