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This content will become publicly available on December 1, 2022

Title: Differentiable Spline Approximations
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}.
Authors:
; ; ; ; ; ; ; ;
Award ID(s):
2053760
Publication Date:
NSF-PAR ID:
10344688
Journal Name:
Advances in neural information processing systems
Volume:
34
Page Range or eLocation-ID:
20270-20282
ISSN:
1049-5258
Sponsoring Org:
National Science Foundation
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