Summary of Regime Learning For Differentiable Particle Filters, by John-joseph Brady and Yuhui Luo and Wenwu Wang and Victor Elvira and Yunpeng Li
Regime Learning for Differentiable Particle Filters
by John-Joseph Brady, Yuhui Luo, Wenwu Wang, Victor Elvira, Yunpeng Li
First submitted to arxiv on: 8 May 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 A novel approach to sequential Monte Carlo techniques is proposed, combining neural networks with particle filters to perform state space inference in systems that switch between multiple regimes. The differentiable particle filter (DPF) and neural network-based regime learning DPF (RLPF) are introduced, along with a training procedure for RLPF and related algorithms. Experimental results show competitive performance compared to previous state-of-the-art methods on two numerical tests. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to use computer models is developed, called the differentiable particle filter. It helps us understand complex systems that change between different states. The new model combines ideas from machine learning and statistical techniques to make predictions about these systems. The approach is tested and shown to be as good as current best practices on two examples. |
Keywords
» Artificial intelligence » Inference » Machine learning » Neural network
