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Algorithmic Differentiation (AD) Beyond Back Propagation

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Back propagation amounts to adjoint AD of neural networks. It is relatively simple to understand and implement. First- and higher-order AD of large-scale numerical simulation programs yields a number of challenges some of which will be discussed during this presentation. Topics to be commented on include the use of symbolic adjoints inside of algorithmic adjoints, the validation of derivative code using differential invariants, and parallel AD.

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