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New paper theorizes discrete diffusion models via "Oracle Distance" theorem

By PulseAugur Editorial · [2 sources] ·

A new paper explores the theoretical underpinnings of discrete diffusion models, proposing the "Oracle Distance" theorem. This theorem equates the negative Evidence Lower Bound (ELBO) to data entropy plus the path KL divergence between the oracle and learned reverse processes. The research identifies three exact coordinates for optimizers—denoiser, cavity, and score—and provides conversion methods among them, unifying various existing loss functions like MDM and UDM. AI

IMPACT Provides a unified theoretical framework for understanding and optimizing discrete diffusion models.

RANK_REASON The cluster contains an academic paper published on arXiv detailing theoretical research in machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New paper theorizes discrete diffusion models via "Oracle Distance" theorem

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Rodrigo Casado Noguerales, Bernhard Sch\"olkopf, Thomas Hofmann, Aran Raoufi ·

    What Does a Discrete Diffusion Model Learn?

    arXiv:2607.05381v1 Announce Type: cross Abstract: What does a discrete diffusion model learn: a denoiser, a score ratio, or a bridge plug-in predictor? At the level of jump rates, these are one object in different coordinates, and reading a neural network in the wrong coordinate …

  2. arXiv stat.ML TIER_1 English(EN) · Aran Raoufi ·

    What Does a Discrete Diffusion Model Learn?

    What does a discrete diffusion model learn: a denoiser, a score ratio, or a bridge plug-in predictor? At the level of jump rates, these are one object in different coordinates, and reading a neural network in the wrong coordinate changes the process being trained and sampled. Sta…

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