Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute
NeurIPS Thirty-fourth Annual Conference on Neural Information Processing Systems (NeuroIPS 2020)
NeurIPS Thirty-fourth Annual Conference on Neural Information Processing Systems (NeurIPS 2020)
December 6-12, 2020
We introduce a new, principled approach to extend gradient-based optimization to piecewise smooth models, such as k-histograms, splines, and segmentation maps. We derive an accurate form of the weak Jacobian of such functions and show that it exhibits a block-sparse structure that can be computed implicitly and efficiently. We show that using the redesigned Jacobian leads to improved performance in applications such as denoising with piecewise polynomial regression models, datafree generative model training, and image segmentation.
Cho, Minsu; Joshi, Ameya; Lee, Xian Yeow; Balu, Aditya; Krishnamurthy, Adarsh; Ganapathysubramanian, Baskar; Sarkar, Soumik; and Hegde, Chinmay, "Differentiable Programming for Piecewise Polynomial Functions" (2020). Mechanical Engineering Conference Presentations, Papers, and Proceedings. 207.