title: RecTable: Fast Modeling Tabular Data with Rectified Flow
summary: 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.
paper_authors._id: 67e5574421895bd1f38b73be
paper_authors.name: Masane Fuchi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T14:25:10.082Z
paper_authors._id: 638f03e5f9fb22b5a073db1b
paper_authors.avatarUrl: /avatars/8b4ae473175e961d0340a6901dabcf29.svg
paper_authors.fullname: Masane Fuchi
paper_authors.type: user
paper_authors.user: fmuuly
paper_authors._id: 67e5574421895bd1f38b73bf
paper_authors.name: Tomohiro Takagi
paper_title: RecTable: Fast Modeling Tabular Data with Rectified Flow
paper_summary: 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.
paper_ai_summary: RecTable, a diffusion model using rectified flow modeling with a simple architecture and training strategies, generates high-quality tabular data more efficiently than existing models.
paper_ai_keywords: score-based models diffusion models GAN-based models VAE-based models rectified flow modeling gated linear unit blocks mixed-type noise distribution logit-normal timestep distribution