mlsanityData to 7 Oct 2026

Stein Neural Sampler

arXiv 1810.03545 ↗First posted 8 Oct 2018stat.ML cs.LGcited by 19

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Abstract (arXiv)

We propose two novel samplers to generate high-quality samples from a given (un-normalized) probability density. Motivated by the success of generative adversarial networks, we construct our samplers using deep neural networks that transform a reference distribution to the target distribution. Training schemes are developed to minimize two variations of the Stein discrepancy, which is designed to work with un-normalized densities. Once trained, our samplers are able to generate samples instantaneously. We show that the proposed methods are theoretically sound and experience fewer convergence issues compared with traditional sampling approaches according to our empirical studies.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Oct 2018.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 6; 2024: 4; 2025: 0; 2026: 1.

19 papers.

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  1. 2023Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Modelscited by 232
  2. 2021Posterior Meta-Replay for Continual Learningcited by 45
  3. 2024Training Neural Samplers with Reverse Diffusive KL Divergencecited by 21
  4. 2022Particle-based Variational Inference with Preconditioned Functional Gradient Flowcited by 20
  5. 2021Neural Variational Gradient Descentcited by 19
  6. 2023Semi-Implicit Variational Inference via Score Matchingcited by 19
  7. 2023Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Accelerationcited by 14
  8. 2023Entropy-based Training Methods for Scalable Neural Implicit Samplercited by 13
  9. 2023Particle-based Variational Inference with Generalized Wasserstein Gradient Flowcited by 10
  10. 2022Neural Stein critics with staged L2-regularizationcited by 2
  11. 2024Semi-Implicit Functional Gradient Flow for Efficient Samplingcited by 1
  12. 2026A Kernel Approach for Semi-implicit Variational Inferencecited by 1
7 more
  1. 2021Generative Particle Variational Inference via Estimation of Functional Gradientscited by 0
  2. 2021A stepped sampling method for video detection using LSTMcited by 0
  3. 2021Adversarial Stein Training for Graph Energy Modelscited by 0
  4. 2022A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generationcited by 0
  5. 2023Arbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transportcited by 0
  6. 2024Functional Gradient Flows for Constrained Samplingcited by 0
  7. 2024Denoising Fisher Training For Neural Implicit Samplerscited by 0