Revisiting semi-supervised training objectives for differentiable particle filters
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929333818359808 |
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| author | Li, Jiaxi Brady, John-Joseph Chen, Xiongjie Li, Yunpeng |
| author_facet | Li, Jiaxi Brady, John-Joseph Chen, Xiongjie Li, Yunpeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revisiting semi-supervised training objectives for differentiable particle filters Li, Jiaxi Brady, John-Joseph Chen, Xiongjie Li, Yunpeng Machine Learning 65C05, 62M20, 62M45, 62M05 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. |
| title | Revisiting semi-supervised training objectives for differentiable particle filters |
| topic | Machine Learning 65C05, 62M20, 62M45, 62M05 |
| url | https://arxiv.org/abs/2405.01251 |