continuous-latent-features

This paper introduces the Function-Space Transformer (FST), a framework that learns from functions over continuous domains by storing features at input-predicted anchor locations with recursive function-space refinement. FST outperforms Perceiver IO on PDEBench prediction, is competitive with Fourier Neural Operators, and beats ViT on ImageNet-1K with fewer parameters.