Implicit Model

Implicit models represent a growing area of machine learning research focused on defining network layers through equilibrium equations rather than explicit layer-by-layer computations. Current research emphasizes efficient training algorithms, particularly for deep equilibrium models and diffusion models, and explores their application in diverse fields like image generation, collaborative filtering, and reinforcement learning. The ability of implicit models to handle complex relationships with reduced computational cost and improved generalization makes them increasingly significant for both theoretical understanding and practical applications across various scientific domains.