mlsanityData to 7 Oct 2026

High-dimensional density estimation with tensorizing flow

arXiv 2212.00759 ↗First posted 1 Dec 2022cs.LG cs.NA math.NAcited by 4

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Abstract (arXiv)

We propose the tensorizing flow method for estimating high-dimensional probability density functions from the observed data. The method is based on tensor-train and flow-based generative modeling. Our method first efficiently constructs an approximate density in the tensor-train form via solving the tensor cores from a linear system based on the kernel density estimators of low-dimensional marginals. We then train a continuous-time flow model from this tensor-train density to the observed empirical distribution by performing a maximum likelihood estimation. The proposed method combines the optimization-less feature of the tensor-train with the flexibility of the flow-based generative models. Numerical results are included to demonstrate the performance of the proposed method.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 33 of its 77 references that are in the map. First posted on arXiv 1 Dec 2022.

Builds on

33 of 77 references.

References · 33

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21 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Ensemble-Based Annealed Importance Samplingcited by 4
  2. 2025Tensorization of neural networks for improved privacy and interpretabilitycited by 3
  3. 2023Combining Monte Carlo and Tensor-network Methods for Partial Differential Equations via Sketchingcited by 0
  4. 2025Density estimation via mixture discrepancy and momentscited by 0