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English(EN) Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning

Mochi模型对齐预训练与推理,实现高效图基础模型

研究人员推出了一种新颖的图基础模型 Mochi,该模型采用元学习框架来增强任务统一性和训练效率。与依赖单独对齐步骤的先前方法不同,Mochi 在直接模拟下游评估协议的少样本(few-shot)试验上进行预训练。这种方法将训练目标与推理对齐,与现有模型相比,可带来更高的性能和显著缩短的训练时间。 AI

影响 Mochi 展示了一种更高效的训练图基础模型的方法,有望降低计算成本并加速基于图的人工智能研究。

排序理由 这是一篇描述新模型和新方法的学术论文。

在 arXiv cs.AI 阅读 →

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Mochi模型对齐预训练与推理,实现高效图基础模型

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jo\~ao Mattos, Arlei Silva ·

    Mochi:通过元学习实现高效图基础模型的预训练和推理对齐

    arXiv:2604.22031v1 Announce Type: new Abstract: We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as link predic…

  2. arXiv cs.AI TIER_1 English(EN) · Arlei Silva ·

    Mochi:通过元学习实现高效图基础模型的预训练和推理对齐

    We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as link prediction, and assume that the resulting representati…