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English(EN) DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning

新的DTA-GT模型通过方向感知增强神经网络架构搜索

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了DTA-GT，一种新颖的方向和拓扑感知图Transformer，旨在改进神经网络表示学习。该模型通过保留计算流语义来解决将神经网络架构表示为有向无环图（DAG）的挑战。DTA-GT利用方向感知初始化、磁拉普拉斯谱编码和方向对偶结构注意力，在NAS-Bench-101和NAS-Bench-201的准确性和延迟预测任务上优于现有预测器。 AI

影响 通过更好地表示计算图，提高了神经网络架构搜索的效率和准确性。

排序理由 该集群包含一篇学术论文，详细介绍了用于神经网络表示学习的新模型和方法。

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

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

新的DTA-GT模型通过方向感知增强神经网络架构搜索

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该集群包含一篇学术论文，详细介绍了用于神经网络表示学习的新模型和方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yuxiang Zeng, Kun Xie, Yang Wang, Jigang Wen, Xiaocan Li, Guangxing Zhang, Gaogang Xie, Jiannong Cao ·

    DTA-GT：面向神经网络表示学习的方向和拓扑感知图Transformer

    arXiv:2610.16822v1 Announce Type: cross Abstract: Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidates. The key difficulty is that neural architectures are operation-labeled DAGs…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Jiannong Cao ·

    DTA-GT：面向神经网络表示学习的方向和拓扑感知图Transformer

    Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or latency from a small set of evaluated candidates. The key difficulty is that neural architectures are operation-labeled DAGs: edge direction defines computational flow, but e…

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