Regime Learning for Differentiable Particle Filters
<|reference_start|>Regime Learning for Differentiable Particle Filters: Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. This paper concerns the case where the system may switch between a finite set of state-space models, i.e. regimes. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. In this paper, we propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem. We further design a training procedure for the RLPF and other related algorithms. We demonstrate competitive performance compared to the previous state-of-the-art algorithms on a pair of numerical experiments.<|reference_end|>
@article{brady2024regime,
    title={Regime Learning for Differentiable Particle Filters},
    author={John-Joseph Brady and Yuhui Luo and Wenwu Wang and Victor Elvira and
  Yunpeng Li},
    journal={arXiv preprint arXiv:2405.04865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04865},
    primaryClass={cs.LG eess.SP}
}