Diff-Joint is a diffusion framework that alternates conditional sampling and uncertainty-aware aggregation to jointly infer imputation values and missingness labels for selective imputation in tabular data.
Missdiff: Training dif- fusion models on tabular data with missing values
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
AugMask is a plug-and-play training framework that lets diffusion models on incomplete tabular data use stochastic augmentation for conditioning and observed-only supervision, outperforming missing-aware baselines via a Rao-Blackwellized objective.
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.
A conditional diffusion model trained on partitioned incomplete samples for physical dynamics achieves asymptotic convergence to the true generative process under mild conditions and outperforms baselines in imputation.
Evaluates 42 variants of foundation models across three formalized paradigms for missing modality reconstruction, identifies shortfalls in semantic extraction and validation, and introduces an agentic framework that reduces FID by at least 14% for images and MER by at least 10% for text.
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How Far Are We from Generating Missing Modalities with Foundation Models?
Evaluates 42 variants of foundation models across three formalized paradigms for missing modality reconstruction, identifies shortfalls in semantic extraction and validation, and introduces an agentic framework that reduces FID by at least 14% for images and MER by at least 10% for text.