mlsanityData to 7 Oct 2026

A General Method for Amortizing Variational Filtering

arXiv 1811.05090 ↗First posted 13 Nov 2018stat.ML cs.LGcited by 14

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Abstract (arXiv)

We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step. By performing each inference optimization procedure with an iterative amortized inference model, we obtain a computationally efficient implementation of the algorithm, which we call amortized variational filtering. We present experiments demonstrating that this general-purpose method improves performance across several deep dynamical latent variable models.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2018.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 3; 2025: 0; 2026: 2.

14 papers.

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  1. 2023Uncovering mesa-optimization algorithms in Transformerscited by 94
  2. 2022Tutorial on amortized optimizationcited by 83
  3. 2022Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiationcited by 46
  4. 2021Online Variational Filtering and Parameter Learningcited by 19
  5. 2023Revisiting Structured Variational Autoencoderscited by 11
  6. 2024Learning Optimal Filters Using Variational Inferencecited by 7
  7. 2023Real-Time Variational Method for Learning Neural Trajectory and its Dynamicscited by 3
  8. 2023Variational excess risk bound for general state space modelscited by 2
  9. 2024Tree-based variational inference for Poisson log-normal modelscited by 2
  10. 2023A Metalearned Neural Circuit for Nonparametric Bayesian Inferencecited by 1
  11. 2024Importance sampling for online variational learningcited by 1
  12. 2026Efficient Online Variational Estimation via Monte Carlo Samplingcited by 1
2 more
  1. 2022Amortized backward variational inference in nonlinear state-space modelscited by 0
  2. 2026Neural Diffusion Intensity Models for Point Process Datacited by 0