RecTable: Fast Modeling Tabular Data with Rectified Flow

• URL: http://arxiv.org/abs/2503.20731v1
• Date: Wed, 26 Mar 2025 17:12:20 GMT
• Title: RecTable: Fast Modeling Tabular Data with Rectified Flow
• Abstract summary: We introduce RecTable, which uses the rectified flow modeling, applied in such as text-to-image generation and text-to-video generation.<n>Our experiments demonstrate that RecTable achieves competitive performance compared to the several state-of-the-art diffusion and score-based models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Score-based or diffusion models generate high-quality tabular data, surpassing GAN-based and VAE-based models. However, these methods require substantial training time. In this paper, we introduce RecTable, which uses the rectified flow modeling, applied in such as text-to-image generation and text-to-video generation. RecTable features a simple architecture consisting of a few stacked gated linear unit blocks. Additionally, our training strategies are also simple, incorporating a mixed-type noise distribution and a logit-normal timestep distribution. Our experiments demonstrate that RecTable achieves competitive performance compared to the several state-of-the-art diffusion and score-based models while reducing the required training time. Our code is available at https://github.com/fmp453/rectable.

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