Revisiting semi-supervised training objectives for differentiable particle filters

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 i...

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Bibliographic Details
Published inProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop pp. 1 - 5
Main Authors Li, Jiaxi, Brady, John-Joseph, Chen, Xiongjie, Li, Yunpeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.07.2024
Subjects
Conferences
differentiable particle filters
Monte Carlo methods
Neural networks
Performance gain
Probabilistic logic
semi-supervised learning
sequential Monte Carlo methods
Signal processing algorithms
Training
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Summary: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.
ISSN:2151-870X
DOI:10.1109/SAM60225.2024.10636571