Separable neural architectures provide a unified framework for both prediction and generation tasks by imposing structural constraints that decompose high-dimensional problems into simpler, more interpretable components—useful when your system has underlying factorizable structure.
This paper introduces separable neural architectures (SNAs), a structured approach to building neural networks that explicitly exploit factorizable patterns in data. By constraining how different parts of a system interact, SNAs can model everything from physics simulations to language more efficiently.