自动图形拓意识的变压器
IEEE transactions on neural networks and learning systems
|September 17, 2024
概括
本研究引入了一个用于设计图形变压器架构的自动化框架,显著减少手工工作和专家知识要求. 进化方法有效地发现各种图表任务的高性能模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 图形转换器为图形数据提供了强大的表示功能.
- 图形变压器架构的手动设计需要大量的专业知识和时间.
- 自动化架构搜索对于高效开发至关重要.
研究的目的:
- 为构建高性能图形变压器提出一个自动化框架.
- 开发一个进化的图形变压器架构搜索 (EGTAS) 方法.
- 为了降低设计有效的图形变压器模型的成本和复杂性.
主要方法:
- 开发了图形变压器的全面搜索空间,包括微层 (策略) 和宏观层 (拓) 的设计.
- 实现了一个进化算法来搜索最佳的图形变压器架构.
- 引入了一个替代模型来有效预测性能,降低评估成本.
主要成果:
- EGTAS框架成功地自动化了强大的图形变压器架构的构建.
- 在各种数据集尺度上,EGTAS在各种图表级和节点级任务中表现出有效性.
- 由EGTAS构建的架构与最先进的手动和自动化方法竞争.
结论:
- EGTAS为自动化图形变压器架构设计提供了有效和高效的解决方案.
- 拟议的框架大大降低了使用先进图形变压器的进入壁垒.
- 自动化架构搜索是推动图形变压器研究和应用的一个有希望的方向.
相关概念视频
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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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However, if this ratio is less than one, the transformer is said to be a step-down...
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