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Poster
Differentiable Spline Approximations
Minsu Cho · Aditya Balu · Ameya Joshi · Anjana Deva Prasad · Biswajit Khara · Soumik Sarkar · Baskar Ganapathysubramanian · Adarsh Krishnamurthy · Chinmay Hegde

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @
in Poster Session 2 »

The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differentiable programming methods (such as autodiff) typically require that the machine learning models be differentiable, limiting their applicability. Our goal in this paper is to use a new, principled approach to extend gradient-based optimization to functions well modeled by splines, which encompass a large family of piecewise polynomial models. We derive the form of the (weak) Jacobian of such functions and show that it exhibits a block-sparse structure that can be computed implicitly and efficiently. Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer'' in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis. We also open-source the code at \url{https://github.com/idealab-isu/DSA}.

Keywords: Optimization · Machine Learning

Author Information

Minsu Cho (New York University)
Aditya Balu (Iowa State University)
Ameya Joshi (Iowa State University)
Anjana Deva Prasad (Iowa State University)
Biswajit Khara (Iowa State University)
Soumik Sarkar (United Technologies Research Center)
Baskar Ganapathysubramanian (Iowa State University)
Adarsh Krishnamurthy (Iowa State University)
Chinmay Hegde (New York University)

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