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Found 10 papers, 4 papers with code
Revisiting semi-supervised training objectives for differentiable particle filters
Differentiable particle filters combine the flexibility of neural networks with the probabilistic nature of sequential Monte Carlo methods.
Normalising Flow-based Differentiable Particle Filters
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.
Learning Differentiable Particle Filter on the Fly
Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models.
Differentiable Bootstrap Particle Filters for Regime-Switching Models
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
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.
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
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.
Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow
Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods.
Differentiable Particle Filters through Conditional Normalizing Flow
Differentiable particle filters provide a flexible mechanism to adaptively train dynamic and measurement models by learning from observed data.
End-To-End Semi-supervised Learning for Differentiable Particle Filters
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
While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost.
