Revisiting semi-supervised training objectives for differentiable particle filters
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Abstract (arXiv)
Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods. However, traditional approaches rely on the availability of labelled data, i.e., the ground truth latent state information, which is often difficult to obtain in real-world applications. This paper compares the effectiveness of two semi-supervised training objectives for differentiable particle filters. We present results in two simulated environments where labelled data are scarce.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 2 May 2024.
Builds on
17 of 30 references.
References · 17
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5 more
- 2021Differentiable Particle Filters through Conditional Normalizing Flowcited by 15
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.