Differentiable particle filtering via entropy-regularized optimal transport
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 fun...
| Hauptverfasser: | , , , |
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| Format: | Conference item |
| Sprache: | English |
| Veröffentlicht: |
Journal of Machine Learning Research
2021
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