Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming

Fei Wang, James Decker, Xilun Wu, Gregory Essertel, Tiark Rompf

Neural Information Processing Systems 

In this paper we propose an implementation of backpropagation using functions with callbacks, where the forward pass is executed as a sequence of function calls, and the backward pass as a corresponding sequence of function returns. A key realization is that this technique of chaining callbacks is well known in the programming languages community as continuation-passing style (CPS) .

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