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Binomial flows enable discrete data denoising and sampling

Researchers have introduced "Binomial flows," a novel framework for generative modeling of discrete ordinal data. This approach bridges the gap between continuous and discrete settings by leveraging Tweedie's formula to connect denoising and sampling processes. The method allows for simultaneous denoising, sampling, and exact likelihood estimation for discrete non-negative ordinal data, showing competitive results on synthetic and real-world datasets. AI

IMPACT Introduces a new method for discrete generative modeling, potentially improving applications involving ordinal data.

RANK_REASON Academic paper introducing a new methodology for generative modeling.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Binomial flows enable discrete data denoising and sampling

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yair Shenfeld, Ricardo Baptista, Stefano Peluchetti ·

    Binomial flows: Denoising and flow matching for discrete ordinal data

    arXiv:2605.00360v1 Announce Type: new Abstract: Flow-based generative modeling in continuous spaces exploit Tweedie's formula to express the denoiser (learned in training) as a score function (used in sampling). In contrast, this relation has been largely missing in the discrete …