Search Results for author: Xiongjie Chen

Found 10 papers, 4 papers with code

Revisiting semi-supervised training objectives for differentiable particle filters

no code implementations2 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.

Normalising Flow-based Differentiable Particle Filters

no code implementations3 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.

Density Estimation Normalising Flows +1

Learning Differentiable Particle Filter on the Fly

no code implementations10 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.

Bayesian Inference Object Tracking +1

Differentiable Bootstrap Particle Filters for Regime-Switching Models

no code implementations20 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.

An overview of differentiable particle filters for data-adaptive sequential Bayesian inference

no code implementations19 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.

Bayesian Inference Sequential Bayesian Inference

Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection

no code implementations26 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.

BIG-bench Machine Learning Out-of-Distribution Detection +2

Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow

1 code implementation16 Mar 2022 Xiongjie Chen, Yunpeng Li

Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods.

Density Estimation valid +1

Differentiable Particle Filters through Conditional Normalizing Flow

1 code implementation1 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.

Visual Tracking

End-To-End Semi-supervised Learning for Differentiable Particle Filters

1 code implementation11 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.

Augmented Sliced Wasserstein Distances

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.

Computational Efficiency valid

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