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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.00360 |
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| _version_ | 1866914524163997696 |
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| author | Shenfeld, Yair Baptista, Ricardo Peluchetti, Stefano |
| author_facet | Shenfeld, Yair Baptista, Ricardo Peluchetti, Stefano |
| contents | 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 setting where common approaches focus on learning discrete scores and rates. In this work we close this gap for discrete non-negative ordinal data by introducing Binomial flows. Our framework provides a simple recipe for training a discrete diffusion model which simultaneously denoises, samples, and estimates exact likelihoods. We verify our methodology on synthetic examples and obtain competitive results on real-world data sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00360 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Binomial flows: Denoising and flow matching for discrete ordinal data Shenfeld, Yair Baptista, Ricardo Peluchetti, Stefano Machine Learning Methodology 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 setting where common approaches focus on learning discrete scores and rates. In this work we close this gap for discrete non-negative ordinal data by introducing Binomial flows. Our framework provides a simple recipe for training a discrete diffusion model which simultaneously denoises, samples, and estimates exact likelihoods. We verify our methodology on synthetic examples and obtain competitive results on real-world data sets. |
| title | Binomial flows: Denoising and flow matching for discrete ordinal data |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2605.00360 |