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Oral
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
Adrien Corenflos · James Thornton · George Deligiannidis · Arnaud Doucet
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF necessary to obtain low variance likelihood and states estimates. However, traditional resampling methods result in PF-based loss functions being non-differentiable with respect to model and PF parameters. In a variational inference context, resampling also yields high variance gradient estimates of the PF-based evidence lower bound. By leveraging optimal transport ideas, we introduce a principled differentiable particle filter and provide convergence results. We demonstrate this novel method on a variety of applications.
Author Information
Adrien Corenflos (Aalto University)
James Thornton (University of Oxford)
George Deligiannidis (Oxford)
Arnaud Doucet (Oxford University)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Poster: Differentiable Particle Filtering via Entropy-Regularized Optimal Transport »
Thu. Jul 22nd 04:00 -- 06:00 PM Room Virtual
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