The paper sketches NFPF, a normalizing-flow particle filter with jointly learned linear latent dynamics, but provides only qualitative and self-admittedly insufficient CartPole experiments.
Differentiable Particle Filters through Conditional Normalizing Flow
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abstract
Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data. However, most existing differentiable particle filters are within the bootstrap particle filtering framework and fail to incorporate the information from latest observations to construct better proposals. In this paper, we utilize conditional normalizing flows to construct proposal distributions for differentiable particle filters, enriching the distribution families that the proposal distributions can represent. In addition, normalizing flows are incorporated in the construction of the dynamic model, resulting in a more expressive dynamic model. We demonstrate the performance of the proposed conditional normalizing flow-based differentiable particle filters in a visual tracking task.
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Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations
The paper sketches NFPF, a normalizing-flow particle filter with jointly learned linear latent dynamics, but provides only qualitative and self-admittedly insufficient CartPole experiments.