一篇新论文证明，广泛的前馈神经网络架构都具有有限样本复杂度。这意味着它们即使在参数无界的情况下，也能在PAC模型中有效学习。研究结果表明，可学习性是许多现代架构的一个基本属性，从而将研究重点转移到归纳偏倚和优化等方面。 AI
影响 将有限样本复杂度确立为许多神经网络架构的一个基本属性，并将研究重点转移到其他架构属性上。
排序理由 该集群包含一篇详细介绍神经网络可学习性理论发现的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
论文表明神经网络具有有限样本复杂度
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- 该集群包含一篇详细介绍神经网络可学习性理论发现的学术论文。
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报道来源 [2]
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o-Minimal结构中可定义的每个前馈神经网络都具有有限样本复杂度
arXiv:2605.07097v1 Announce Type: new Abstract: We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture whose layers are definable in an o-minimal structure …
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o-Minimal结构中可定义的每个前馈神经网络都具有有限样本复杂度
We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture whose layers are definable in an o-minimal structure has finite sample complexity in the agnostic PAC…