First dedicated survey organizing diffusion and flow matching models for tabular data synthesis, imputation, anomaly detection, and related tasks, covering literature from 2015 to 2026 and highlighting open problems.
Unleashing the potential of diffusion models for incomplete data imputation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
CoreFlow is a low-rank matrix generative model that trains normalizing flows on shared subspaces to improve efficiency and quality for high-dimensional limited-sample data, including incomplete matrices.
A VAE-based latent diffusion model trained on incomplete data maintains sample quality and imputation performance up to 50% missingness while pixel-space diffusion degrades.
citing papers explorer
-
Diffusion and Flow Matching Models for Tabular Data: A Survey
First dedicated survey organizing diffusion and flow matching models for tabular data synthesis, imputation, anomaly detection, and related tasks, covering literature from 2015 to 2026 and highlighting open problems.
-
CoreFlow: Low-Rank Matrix Generative Models
CoreFlow is a low-rank matrix generative model that trains normalizing flows on shared subspaces to improve efficiency and quality for high-dimensional limited-sample data, including incomplete matrices.
-
Latent Diffusion for Missing Data
A VAE-based latent diffusion model trained on incomplete data maintains sample quality and imputation performance up to 50% missingness while pixel-space diffusion degrades.