The main idea of Automatic Differentiation (AD) is to treat a computation as a nested sequence of function compositions, calculating derivatives using the chain rule.
Reverse mode AD generalizes backpropagation for neural networks, allowing derivative computation from multiple outputs rather than a single scalar.
Understanding the chain rule of calculus, especially its multivariate formulation, is essential for grasping the concepts outlined in the ADIMLAS paper.
In using AD, computations can be represented as linear chain graphs, which helps visualize and organize the derivative calculations effectively.
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