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New research details SGD convergence for score-based generative models

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper detailing the non-asymptotic convergence of Stochastic Gradient Descent (SGD) when applied to Score-based Generative Models (SGMs). The study provides theoretical guarantees for SGD in training SGMs, addressing the optimization dynamics which have been less explored than their sampling procedures. The work establishes convergence rates for general score parameterizations and uses a Neural Tangent Kernel analysis for overparameterized networks, offering guidance on weighting choices in practice. AI

IMPACT Provides theoretical guidance for optimizing score-based generative models, potentially improving their training efficiency and performance.

RANK_REASON The cluster contains a newly published academic paper on a machine learning topic.

Read on arXiv stat.ML →

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

New research details SGD convergence for score-based generative models

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Stanislas Strasman (SU, LPSM), Sobihan Surendran (SU, LPSM), Sylvain Le Corff (SU, LPSM) ·

    Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

    arXiv:2607.04775v1 Announce Type: new Abstract: Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling procedures are increasingly well understood, the op…

  2. arXiv stat.ML TIER_1 English(EN) · Sylvain Le Corff ·

    Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

    Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling procedures are increasingly well understood, the optimization dynamics underlying their training re…

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