机器学习与树张量网络,CP排名约束和张量脱落
概括
受物理学启发的张量网络使用受约束的张量级来实现高效的机器学习. 这种方法可以提高图像分类的准确性,同时降低计算成本,避免常见的深度学习问题.
科学领域:
- 机器学习 机器学习
- 计算物理 计算物理
- 数据科学数据科学数据科学
背景情况:
- 张量网络使用减少的自由度近似大张量.
- 之前已经证明,正规的多体 (CP) 级别约束可以降低量子多体物理学中的计算成本.
研究的目的:
- 在机器学习中展示树张量网络 (TTN) 与CP等级约束和张量脱落的应用.
- 评估这种方法在图像分类任务中的性能.
主要方法:
- 使用树张量网络 (TTN) 强加CP等级约束.
- 作为规范化技术的纳入式张量脱落.
- 将TTN分类器应用于时尚-MNIST数据集.
主要成果:
- 具有CP等级约束和张量脱落的TTN分类器优于其他基于张量网络的方法.
- 在时尚-MNIST上使用低级TTN,分支比为4.3的TTN,达到90.3%的测试集准确度.
- 与传统方法相比,演示了较低的计算成本.
结论:
- 张量网络分类器,特别是具有CP等级约束的TTNs,为深度神经网络提供了有效的替代方案.
- CP等级约束提高了概括性,控制了过拟合,减少了参数,降低了计算成本.
- 提出的方法有效地解决了深度神经网络固有的消失梯度问题.
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