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New diffusion models tackle discrete ordinal data with bi-directional perturbations

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new family of diffusion models called Jumping Up and Down (JUD) specifically designed for discrete ordinal data. This novel approach centers on training denoisers and allows for bi-directional perturbations of the data, a capability not previously seen in diffusion models for ordinal data. The JUD models achieve competitive results across various data modalities, including images, music, and gene counts, marking a significant advancement in the field. AI

IMPACT Introduces a new class of diffusion models for ordinal data, potentially improving performance in areas like image and gene count analysis.

RANK_REASON The cluster contains a new academic paper detailing a novel diffusion model for discrete ordinal data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New diffusion models tackle discrete ordinal data with bi-directional perturbations

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The cluster contains a new academic paper detailing a novel diffusion model for discrete ordinal data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Jumping up and down: Denoiser diffusion models for discrete ordinal data

    arXiv:2610.02754v1 Announce Type: new Abstract: Diffusion models are highly developed in continuous spaces for image and video domains. Recently, major advances have been made for discrete diffusion models for categorical data, specifically in the language domain. In contrast, di…

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