English(EN) Revisiting Neural Processes via Fourier Transform and Volterra Series

新的神经过程方法利用傅里叶和沃尔泰拉级数

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-31 11:27

研究人员开发了改进神经过程（NP）的新方法，NP 是一种用于从有限数据进行函数逼近的概率模型。他们的工作通过引入集合傅里叶卷积（SFConvs）并利用沃尔泰拉展开来解决现有平移等变神经过程的局限性。这些创新使模型能够处理具有全局感受野和线性缩放的非规则采样点，从而提供更好的可解释性和效率。 AI

影响引入了函数逼近的新技术，有可能提高科学和工程应用中的性能。

排序理由该集群包含一篇详细介绍神经过程新方法的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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Newsworthiness bucket: Research; 该集群包含一篇详细介绍神经过程新方法的学术论文。
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arXiv stat.ML TIER_1 English(EN) · Peiman Mohseni, Nick Duffield, Raymond K. W. Wong · 2026-06-02 04:00

通过傅里叶变换和Volterra级数重新审视神经过程

arXiv:2606.01172v1 Announce Type: cross Abstract: Modeling unknown latent functions from finite, irregularly sampled measurements is a recurring challenge across science and engineering. Neural processes (NPs), a family of probabilistic functional models, are promising solutions …
arXiv stat.ML TIER_1 English(EN) · Raymond K. W. Wong · 2026-05-31 11:27

通过傅里叶变换和Volterra级数重新审视神经过程

Modeling unknown latent functions from finite, irregularly sampled measurements is a recurring challenge across science and engineering. Neural processes (NPs), a family of probabilistic functional models, are promising solutions -- especially when endowed with domain-specific sy…