Summary of Normalizing Flow-based Differentiable Particle Filters, by Xiongjie Chen et al.
Normalizing Flow-based Differentiable Particle Filters
by Xiongjie Chen, Yunpeng Li
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to differentiable particle filtering for joint sequential state estimation and model learning in complex environments. The authors propose a framework that utilizes conditional normalizing flows to build its dynamic model, proposal distribution, and measurement model. This allows the method to adaptively learn these modules without being restricted to predefined distribution families. The proposed filters are theoretically sound and are evaluated through numerical experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve upon existing differentiable particle filters by allowing density estimation and adaptable learning. It presents a framework that uses normalizing flows to build its models, enabling valid probability densities and flexible adaptation. This approach could be useful in complex real-world scenarios where traditional methods may struggle. |
Keywords
* Artificial intelligence * Density estimation * Probability
