Implicit Differentiation

Implicit differentiation is a technique for calculating derivatives of functions defined implicitly, often arising in complex systems where direct differentiation is intractable. Current research focuses on applying this technique to diverse areas, including solving partial differential equations using neural networks (e.g., physics-informed neural networks and graph neural networks), optimizing hyperparameters in machine learning models, and training neural networks with non-differentiable components (e.g., binarized networks and spiking neural networks). These advancements improve the efficiency and scalability of training and optimization processes across various fields, leading to more accurate and robust models in applications ranging from image processing and fluid dynamics to natural language processing and Bayesian inference.