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Extending Pathwise Gradients to Discrete Random Variables via Finite-Order Relaxation
Pathwise gradients are preferred for continuous random variables because they are unbiased, low variance, and work with a single sample. For discrete variables, however, the pathwise identity cannot generally be exact for every differentiable function. We propose a general framework to construct fin
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