mlsanityData to 7 Oct 2026

Improving Explorability in Variational Inference with Annealed Variational Objectives

arXiv 1809.01818 ↗First posted 6 Sep 2018cs.LG stat.MLcited by 24

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Abstract (arXiv)

Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned. We demonstrate the drawbacks of biasing the true posterior to be unimodal, and introduce Annealed Variational Objectives (AVO) into the training of hierarchical variational methods. Inspired by Annealed Importance Sampling, the proposed method facilitates learning by incorporating energy tempering into the optimization objective. In our experiments, we demonstrate our method's robustness to deterministic warm up, and the benefits of encouraging exploration in the latent space.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2018.

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Citing papers per year: 2021: 9; 2022: 5; 2023: 4; 2024: 2; 2025: 1; 2026: 3.

24 papers.

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  1. 2021Annealed Flow Transport Monte Carlocited by 90
  2. 2022Score-Based Diffusion meets Annealed Importance Samplingcited by 51
  3. 2021MCMC Variational Inference via Uncorrected Hamiltonian Annealingcited by 39
  4. 2021Monte Carlo Variational Auto-Encoderscited by 36
  5. 2021Annealed Stein Variational Gradient Descentcited by 24
  6. 2022Posterior Collapse of a Linear Latent Variable Modelcited by 18
  7. 2021On the Robustness to Misspecification of α-Posteriors and Their Variational Approximationscited by 15
  8. 2021Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluationcited by 15
  9. 2021Nested Variational Inferencecited by 14
  10. 2023Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Networkcited by 7
  11. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  12. 2023Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoderscited by 6
12 more
  1. 2023Semiparametric inference using fractional posteriorscited by 4
  2. 2025Posterior Collapse as a Phase Transition in Variational Autoencoderscited by 4
  3. 2022AdaAnn: Adaptive Annealing Scheduler for Probability Density Approximationcited by 3
  4. 2022Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAEcited by 3
  5. 2024BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptationcited by 3
  6. 2026Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixturescited by 2
  7. 2021Variational Laplace for Bayesian neural networkscited by 1
  8. 2022Variational Meta Reinforcement Learning for Social Roboticscited by 1
  9. 2023Learning Sparse Codes with Entropy-Based ELBOscited by 0
  10. 2024Variational Sampling of Temporal Trajectoriescited by 0
  11. 2026Quasi Monte Carlo methods enable extremely low-dimensional deep generative modelscited by 0
  12. 2026Spherical VAE with Cluster-Aware Feasible Regions: Guaranteed Prevention of Posterior Collapsecited by 0