Speaker
Description
I will talk about our recent works on automatic differentiation algorithms for differentiating low-dimensional integrals of discontinuous functions, which are common in computer graphics, vision, and machine learning applications. Our method makes minimal assumptions on the program structure and does not require specialized routines for discretizing the integrals. We achieve this by directly sampling the Dirac delta distributions that arise at the decision boundaries. We then apply a program transformation that converts discontinuous functions into piecewise constant ones, this program transformation enables an efficient boundary sampling through a segment sampling technique. We show several applications in computer graphics, and I will talk about potential applications outside of graphics.