Mode Automatic Differentiation

Mode automatic differentiation (AD) encompasses techniques for calculating derivatives of functions, primarily focusing on forward-mode approaches as alternatives to the more common reverse-mode (backpropagation). Current research emphasizes developing efficient forward-mode AD algorithms for various model architectures, including neural networks (both feedforward and recurrent), and their application to optimization problems, federated learning, and data-driven modeling. This focus stems from the advantages of forward-mode AD in terms of reduced memory footprint and potential for improved scalability, particularly beneficial for large models and resource-constrained environments. The resulting advancements promise to enhance the efficiency and applicability of machine learning across diverse domains.