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English(EN) Emergent Models: Intelligence from Tiny Substrates

涌现模型：新的机器学习范式利用简单系统实现智能

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇新的研究论文介绍了一种名为“涌现模型”（EMs）的机器学习范式，它不同于传统的输入-输出映射。相反，EMs 通过进化搜索进行训练，专注于简单动力系统中计算行为的涌现。作者假设这种方法可以实现全局泛化，并证明一些 EMs 是潜在通用的，能够实现任何部分可计算函数。在展示了小型 EMs 可以外推算术函数并支持适应性的同时，该论文也承认了它们目前的局限性，并将 EMs 定位为一个拓宽机器学习设计范围的框架，超越了可微分的前馈网络。 AI

影响 这项研究提出了一个新的机器学习框架，有可能将设计可能性扩展到当前可微分模型之外。

排序理由 该集群包含一篇详细介绍新机器学习范式的研究论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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涌现模型：新的机器学习范式利用简单系统实现智能

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Giacomo Bocchese, Nicola Giacobbo, Etienne Guichard, James Wiles, Akshaj Devireddy ·

    涌现模型：微小基底中的智能

    arXiv:2608.14019v1 Announce Type: cross Abstract: Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treated not as the learning of a closed-form input-output map but as the emergence, w…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Akshaj Devireddy ·

    涌现模型：微小基底中的智能

    Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treated not as the learning of a closed-form input-output map but as the emergence, within simple dynamical systems, of computational b…

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