mlsanityData to 14 Oct 2026

Particle Filter Recurrent Neural Networks

arXiv 1905.12885 ↗First posted 30 May 2019cs.LG stat.MLcited by 36

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Abstract (arXiv)

Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particle Filter Recurrent Neural Networks (PF-RNNs), a new RNN family that explicitly models uncertainty in its internal structure: while an RNN relies on a long, deterministic latent state vector, a PF-RNN maintains a latent state distribution, approximated as a set of particles. For effective learning, we provide a fully differentiable particle filter algorithm that updates the PF-RNN latent state distribution according to the Bayes rule. Experiments demonstrate that the proposed PF-RNNs outperform the corresponding standard gated RNNs on a synthetic robot localization dataset and 10 real-world sequence prediction datasets for text classification, stock price prediction, etc.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 May 2019.

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Citing papers per year: 2021: 12; 2022: 5; 2023: 7; 2024: 7; 2025: 2; 2026: 3.

36 papers.

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  1. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
  2. 2021Differentiable SLAM-net: Learning Particle SLAM for Visual Navigationcited by 34
  3. 2023An overview of differentiable particle filters for data-adaptive sequential Bayesian inferencecited by 23
  4. 2021Structured World Belief for Reinforcement Learning in POMDPcited by 21
  5. 2022Multi-scale Attention Flow for Probabilistic Time Series Forecastingcited by 19
  6. 2021Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objectscited by 17
  7. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  8. 2022Flow-based Recurrent Belief State Learning for POMDPscited by 16
  9. 2021Differentiable Particle Filters through Conditional Normalizing Flowcited by 15
  10. 2021Learning to Assimilate in Chaotic Dynamical Systemscited by 10
  11. 2024Normalizing Flow-based Differentiable Particle Filterscited by 10
  12. 2021Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filterscited by 9
24 more
  1. 2023The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Modelscited by 9
  2. 2023Unsupervised Learning of Sampling Distributions for Particle Filterscited by 8
  3. 2021Variational Marginal Particle Filterscited by 6
  4. 2021Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenescited by 5
  5. 2022Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placementcited by 5
  6. 2024Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensorscited by 5
  7. 2021Unrolling Particles: Unsupervised Learning of Sampling Distributionscited by 4
  8. 2024A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systemscited by 4
  9. 2022Particle-Based Score Estimation for State Space Model Learning in Autonomous Drivingcited by 3
  10. 2024PF-GNN: Differentiable particle filtering based approximation of universal graph representationscited by 3
  11. 2025Model-Based Reinforcement Learning under Random Observation Delayscited by 3
  12. 2023Differentiable Bootstrap Particle Filters for Regime-Switching Modelscited by 2
  13. 2024Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbationscited by 2
  14. 2025DiffPF: Differentiable Particle Filtering with Generative Sampling via Conditional Diffusion Modelscited by 2
  15. 2023Dynamic Analysis and an Eigen Initializer for Recurrent Neural Networkscited by 1
  16. 2021Recursive Least Squares Based Refinement Network for the Rollout Trajectory Prediction Methodscited by 0
  17. 2022Hidden State Approximation in Recurrent Neural Networks Using Continuous Particle Filteringcited by 0
  18. 2023Memory-efficient particle filter recurrent neural network for object localizationcited by 0
  19. 2023From Deep Filtering to Deep Econometricscited by 0
  20. 2024Inverse Particle Filtercited by 0
  21. 2024Learning Flock: Enhancing Sets of Particles for Multi~Sub-State Particle Filtering with Neural Augmentationcited by 0
  22. 2026Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecastingcited by 0
  23. 2026Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensorscited by 0
  24. 2026A Unified Particle Filter LSTM for Data-Driven Process Simulationcited by 0