Implicit Differentiation

An approach for computing derivatives through solutions or equilibrium conditions that are not written explicitly.

Implicit differentiation becomes important especially in optimization layers, equilibrium models, and advanced differentiable systems. In some structures, the output is not represented in closed form but is instead defined through a solution condition, and derivatives are computed through that condition. This shows that deep learning can be built not only from classical layers, but from more general computation blocks. It is an important tool in advanced research and differentiable programming.