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English(EN) Operator-Theoretic Generalization Bounds for Multitask Deep Learning

多任务深度学习的新算子理论界

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员为深度多任务学习模型开发了算子理论泛化界。该方法将网络层表示为向量值再生核希尔伯特空间中的Koopman组合算子。该方法为特定的网络架构产生了Rademacher复杂性界，并区分了输出耦合和层算子范数的贡献。研究还探讨了一维布朗/Cameron--Martin模型，提供了不依赖于Sobolev光滑指数的层界。 AI

影响 这项研究可能导致多任务深度学习模型中更强的泛化能力。

排序理由 该集群包含一篇详细介绍深度学习理论进展的学术论文。

在 Hugging Face Daily Papers 阅读 →

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多任务深度学习的新算子理论界

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mahdi Mohammadigohari, Thomas Borsani, Giuseppe Di Fatta ·

    Operator-Theoretic Generalization Bounds for Multitask Deep Learning

    arXiv:2608.15982v1 Announce Type: new Abstract: We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces. In vector-valued Sobolev …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    面向多任务深度学习的算子理论泛化界限

    We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces. In vector-valued Sobolev RKHSs, we derive Rademacher complexity bounds fo…

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