Deep Equilibrium

Deep equilibrium (DEQ) models represent a novel approach in neural networks, aiming to improve efficiency and stability by replacing multiple layers with a single implicit layer whose output is defined as the solution to a fixed-point equation. Current research focuses on applying DEQs to diverse problems, including solving differential equations, predicting density functional theory Hamiltonians, and accelerating various machine learning tasks, often utilizing architectures like the Generative Equilibrium Transformer (GET). This approach offers significant advantages in memory efficiency and computational speed, impacting fields such as materials science, computer vision, and scientific computing through faster and more robust solutions to complex problems.