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.
Funnels: Exact maximum likelihood with dimensionality reduction
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abstract
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing surjective flows via a new layer, known as the funnel. We demonstrate its efficacy on a variety of datasets, and show it improves upon or matches the performance of existing flows while having a reduced latent space size. The funnel layer can be constructed from a wide range of transformations including restricted convolution and feed forward layers.
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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.