no code implementations • 2 May 2024 • Jiaxi Li, John-Joseph Brady, Xiongjie Chen, Yunpeng Li
Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods.
no code implementations • 3 Mar 2024 • Xiongjie Chen, Yunpeng Li
Recently, there has been a surge of interest in incorporating neural networks into particle filters, e. g. differentiable particle filters, to perform joint sequential state estimation and model learning for non-linear non-Gaussian state-space models in complex environments.
no code implementations • 10 Dec 2023 • Jiaxi Li, Xiongjie Chen, Yunpeng Li
Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models.
no code implementations • 20 Feb 2023 • Wenhan Li, Xiongjie Chen, Wenwu Wang, Víctor Elvira, Yunpeng Li
Differentiable particle filters are an emerging class of particle filtering methods that use neural networks to construct and learn parametric state-space models.
no code implementations • 19 Feb 2023 • Xiongjie Chen, Yunpeng Li
Due to the expressiveness of neural networks, differentiable particle filters are a promising computational tool for performing inference on sequential data in complex, high-dimensional tasks, such as vision-based robot localisation.
no code implementations • 26 Jun 2022 • Xiongjie Chen, Yunpeng Li, Yongxin Yang
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications.
1 code implementation • 16 Mar 2022 • Xiongjie Chen, Yunpeng Li
Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods.
1 code implementation • 1 Jul 2021 • Xiongjie Chen, Hao Wen, Yunpeng Li
Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data.
1 code implementation • 11 Nov 2020 • Hao Wen, Xiongjie Chen, Georgios Papagiannis, Conghui Hu, Yunpeng Li
Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications.
1 code implementation • ICLR 2022 • Xiongjie Chen, Yongxin Yang, Yunpeng Li
While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost.