mlsanityData to 7 Oct 2026

Neural Adaptive Sequential Monte Carlo

arXiv 1506.03338 ↗First posted 10 Jun 2015cs.LG stat.MLcited by 34

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Abstract (arXiv)

Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions. Like other importance sampling-based methods, performance is critically dependent on the proposal distribution: a bad proposal can lead to arbitrarily inaccurate estimates of the target distribution. This paper presents a new method for automatically adapting the proposal using an approximation of the Kullback-Leibler divergence between the true posterior and the proposal distribution. The method is very flexible, applicable to any parameterized proposal distribution and it supports online and batch variants. We use the new framework to adapt powerful proposal distributions with rich parameterizations based upon neural networks leading to Neural Adaptive Sequential Monte Carlo (NASMC). Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive proposal methods including the Extended Kalman and Unscented Particle Filters. Experiments also indicate that improved inference translates into improved parameter learning when NASMC is used as a subroutine of Particle Marginal Metropolis Hastings. Finally we show that NASMC is able to train a latent variable recurrent neural network (LV-RNN) achieving results that compete with the state-of-the-art for polymorphic music modelling. NASMC can be seen as bridging the gap between adaptive SMC methods and the recent work in scalable, black-box variational inference.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2015.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 5; 2024: 7; 2025: 4; 2026: 4.

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  1. 2024Probabilistic Inference in Language Models via Twisted Sequential Monte Carlocited by 85
  2. 2021Challenges and Opportunities in High-dimensional Variational Inferencecited by 35
  3. 2022SIXO: Smoothing Inference with Twisted Objectivescited by 22
  4. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  5. 2022Critic Sequential Monte Carlocited by 16
  6. 2022On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learningcited by 13
  7. 2021HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Samplingcited by 12
  8. 2021Learning to Assimilate in Chaotic Dynamical Systemscited by 10
  9. 2024KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertaintycited by 9
  10. 2022Low-Rank Constraints for Fast Inference in Structured Modelscited by 8
  11. 2022Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transportcited by 8
  12. 2021A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authenticationcited by 7
22 more
  1. 2024Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inferencecited by 7
  2. 2022Recurrent Neural Networks and Universal Approximation of Bayesian Filterscited by 5
  3. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  4. 2024Quasi-Bayes meets Vinescited by 5
  5. 2025Trust-Region Twisted Policy Improvementcited by 5
  6. 2023NAS-X: Neural Adaptive Smoothing via Twistingcited by 4
  7. 2023Online Variational Sequential Monte Carlocited by 4
  8. 2024SPO: Sequential Monte Carlo Policy Optimisationcited by 4
  9. 2024Probabilistic Programming with Programmable Variational Inferencecited by 4
  10. 2025Tracking large chemical reaction networks and rare events by neural networkscited by 3
  11. 2023The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flowscited by 2
  12. 2025Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coveragecited by 2
  13. 2021Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Seriescited by 1
  14. 2022Optimization of Annealed Importance Sampling Hyperparameterscited by 1
  15. 2021Fast ABC with joint generative modelling and subset simulationcited by 0
  16. 2023Worst-Case Analysis is Maximum-A-Posteriori Estimationcited by 0
  17. 2024Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networkscited by 0
  18. 2025Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Spacecited by 0
  19. 2026VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learningcited by 0
  20. 2026Generative Model Proposal based Particle Filtering for Data Assimilationcited by 0
  21. 2026Learning to Bias: Machine Learning-Enhanced Particle Filterscited by 0
  22. 2026One Proposal for Every Margin: Zero-Shot Amortized Sequential Importance Sampling for Binary Matricescited by 0