Method of Adjoints for differentiating through ODEs
September 15, 2017 — May 14, 2023
Bayes
dynamical systems
linear algebra
probability
signal processing
state space models
statistics
time series
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Constructing a backward (P)DE which effectively gives us the gradients of the forward (P)DE. A trick in automatic differentiation which happens to be useful in differentiating likelihood (or other functions) of time-evolving systems. This is an active area of research (Kidger, Chen, and Lyons 2021; Kidger et al. 2020; Li et al. 2020; Rackauckas et al. 2018; Stapor, Fröhlich, and Hasenauer 2018; Cao et al. 2003), but also old and well-studied [Errico (1997);
- versus autodiff: There and Back Again: A Tale of Slopes and Expectations | Mathematics for Machine Learning
1 References
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Kidger, Chen, and Lyons. 2021. “‘Hey, That’s Not an ODE’: Faster ODE Adjoints via Seminorms.” In Proceedings of the 38th International Conference on Machine Learning.
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