mlsanityData to 7 Oct 2026

Continual Repeated Annealed Flow Transport Monte Carlo

arXiv 2201.13117 ↗First posted 31 Jan 2022stat.ML cond-mat.stat-mech cs.LGcited by 66

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Abstract (arXiv)

We propose Continual Repeated Annealed Flow Transport Monte Carlo (CRAFT), a method that combines a sequential Monte Carlo (SMC) sampler (itself a generalization of Annealed Importance Sampling) with variational inference using normalizing flows. The normalizing flows are directly trained to transport between annealing temperatures using a KL divergence for each transition. This optimization objective is itself estimated using the normalizing flow/SMC approximation. We show conceptually and using multiple empirical examples that CRAFT improves on Annealed Flow Transport Monte Carlo (Arbel et al., 2021), on which it builds and also on Markov chain Monte Carlo (MCMC) based Stochastic Normalizing Flows (Wu et al., 2020). By incorporating CRAFT within particle MCMC, we show that such learnt samplers can achieve impressively accurate results on a challenging lattice field theory example.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

19 of 39 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2016Density estimation using Real NVPcited by 2,029
  4. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  5. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
  6. 2020Stochastic Normalizing Flowscited by 156
  7. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  8. 2017Variational Sequential Monte Carlocited by 93
  9. 2017Filtering Variational Objectivescited by 91
  10. 2021Annealed Flow Transport Monte Carlocited by 90
  11. 2017Auto-Encoding Sequential Monte Carlocited by 64
  12. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
7 more
  1. 2021MCMC Variational Inference via Uncorrected Hamiltonian Annealingcited by 39
  2. 2021Monte Carlo Variational Auto-Encoderscited by 36
  3. 2021Differentiable Annealed Importance Sampling and the Perils of Gradient Noisecited by 36
  4. 2021Introduction to Normalizing Flows for Lattice Field Theorycited by 35
  5. 2020An invitation to sequential Monte Carlo samplerscited by 33
  6. 2021Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flowscited by 24
  7. 2021Nested Variational Inferencecited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 9; 2024: 21; 2025: 23; 2026: 9.

66 papers.

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  1. 2022Flow Annealed Importance Sampling Bootstrapcited by 124
  2. 2024Iterated Denoising Energy Matching for Sampling from Boltzmann Densitiescited by 113
  3. 2025Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matchingcited by 78
  4. 2024NETS: A Non-Equilibrium Transport Samplercited by 67
  5. 2024Particle Denoising Diffusion Samplercited by 63
  6. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  7. 2024Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Samplingcited by 55
  8. 2022Score-Based Diffusion meets Annealed Importance Samplingcited by 51
  9. 2024Sequential Controlled Langevin Diffusionscited by 47
  10. 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generatorscited by 36
  11. 2025LEAPS: A discrete neural sampler via locally equivariant networkscited by 30
  12. 2025Underdamped Diffusion Bridges with Applications to Samplingcited by 28
38 more
  1. 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
  2. 2024Liouville Flow Importance Samplercited by 25
  3. 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
  4. 2023Transport meets Variational Inference: Controlled Monte Carlo Diffusionscited by 21
  5. 2025Adjoint Schr"odinger Bridge Samplercited by 20
  6. 2024Dynamical Measure Transport and Neural PDE Solvers for Samplingcited by 19
  7. 2025From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingcited by 17
  8. 2025Proximal Diffusion Neural Samplercited by 16
  9. 2025Temperature-Annealed Boltzmann Generatorscited by 14
  10. 2023Entropy-based Training Methods for Scalable Neural Implicit Samplercited by 13
  11. 2025Accelerated Parallel Tempering via Neural Transportscited by 13
  12. 2024Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributionscited by 11
  13. 2025Amortized Sampling with Transferable Normalizing Flowscited by 11
  14. 2024Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flowscited by 9
  15. 2024Learned Reference-based Diffusion Sampling for multi-modal distributionscited by 9
  16. 2025Non-equilibrium Annealed Adjoint Samplercited by 8
  17. 2025Learning Boltzmann Generators via Constrained Mass Transportcited by 8
  18. 2024Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inferencecited by 7
  19. 2024Importance Corrected Neural JKO Samplingcited by 7
  20. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  21. 2025Scaling flow-based approaches for topology sampling in SU(3) gauge theorycited by 6
  22. 2022Proximal Residual Flows for Bayesian Inverse Problemscited by 5
  23. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  24. 2023Estimating Gibbs free energies via isobaric-isothermal flowscited by 5
  25. 2024Stable Training of Normalizing Flows for High-dimensional Variational Inferencecited by 5
  26. 2024Fast and Unified Path Gradient Estimators for Normalizing Flowscited by 5
  27. 2024Quasi-Bayes meets Vinescited by 5
  28. 2024Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VIcited by 5
  29. 2025Neural Flow Samplers with Shortcut Modelscited by 5
  30. 2025End-To-End Learning of Gaussian Mixture Priors for Diffusion Samplercited by 4
  31. 2026Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processescited by 4
  32. 2025Model-Informed Flows for Bayesian Inferencecited by 3
  33. 2023The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flowscited by 2
  34. 2023Reparameterized Variational Rejection Samplingcited by 2
  35. 2023Rare Event Probability Learning by Normalizing Flowscited by 2
  36. 2025SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flowscited by 2
  37. 2024Empirical evaluation of normalizing flows in Markov Chain Monte Carlocited by 1
  38. 2025Asymptotically exact variational flows via involutive MCMC kernelscited by 1

50 most cited of 66 All 66 (JSON)