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English(EN) Transferable Graph Metanetworks

新型图元网络实现跨架构模型转换

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了图元网络（GMNs），可以直接操作神经网络的参数，从而实现预测模型属性和转换已训练模型等任务。CrossGMN模型通过处理源网络和目标网络来专门解决跨架构转换问题，例如模型压缩。这种方法可以加快知识蒸馏的速度，并且无需重新训练即可在不同数据集之间进行迁移。此外，可迁移图元网络增强了GMNs，以在不同网络宽度下实现更好的泛化能力，尤其是在最大更新参数化（$\mu$P）下训练的网络。 AI

影响 图元网络的这些进展可能会加速模型压缩，并提高学习到的属性在不同神经网络架构之间的可迁移性。

排序理由 该集群包含两篇详细介绍图元网络新方法的学术论文。

在 arXiv stat.ML 阅读 →

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新型图元网络实现跨架构模型转换

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该集群包含两篇详细介绍图元网络新方法的学术论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Adir Dayan, Yam Eitan, Haggai Maron ·

    CrossGMN：用于跨架构权重空间转换的图元网络

    arXiv:2610.01649v1 Announce Type: new Abstract: Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained models, and generating weights. Weight-space symmetries such as neuron permutations m…

  2. arXiv stat.ML TIER_1 English(EN) · Yuxin Ma, Adir Dayan, Yam Eitan, Haggai Maron, Soledad Villar ·

    可迁移图元网络

    arXiv:2610.00420v1 Announce Type: new Abstract: A weight space network (or metanetwork) takes the weights of another neural network as input and predicts properties of it. Most prior work trains such models on input networks of one or a few fixed sizes and evaluates them in-distr…

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