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English(EN) Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training

新的PUMA方法加速了遮蔽扩散模型的训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为渐进式解遮蔽（Progressive UnMAsking, PUMA）的新方法，用于加速遮蔽扩散模型（MDMs）的训练。PUMA使训练期间使用的遮蔽模式与推理期间使用的模式保持一致，从而将优化集中在更相关的遮蔽上。该方法在预训练方面显示出显著的加速效果，在1.25亿参数规模下，训练时间大约快了2.5倍。 AI

影响 加速了扩散模型的训练，可能促进生成式AI的更快迭代和开发。

排序理由 该集群包含一篇详细介绍扩散模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的PUMA方法加速了遮蔽扩散模型的训练

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该集群包含一篇详细介绍扩散模型训练新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jaeyeon Kim, Jonathan Geuter, David Alvarez-Melis, Sham Kakade, Sitan Chen ·

    停止为最坏情况训练：渐进式解遮蔽加速了遮蔽扩散模型训练

    arXiv:2602.10314v2 Announce Type: replace Abstract: Masked Diffusion Models (MDMs) have emerged as a promising approach for generative modeling in discrete spaces. By generating sequences in any order and allowing for parallel decoding, they enable fast inference and strong perfo…

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