新的优化器增强了用于机器学习任务的树张量网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员为树张量网络（TTNs）开发了新的随机黎曼优化器，TTNs 是一种源自量子物理学的模型类型，在机器学习应用中显示出潜力。这些优化器专为参数和商流形设计，并结合了适用于小批量训练的自适应和无学习率策略。当应用于混合 CNN-TTN 架构时，这些方法在 Fashion-MNIST、CIFAR-10 和 Imagenette 等数据集上展示了与无约束优化相当的性能，同时还促进了稳定的数值压缩。 AI

影响引入了张量网络的新型优化技术，有望提高机器学习模型的效率和稳定性。

排序理由该项目是一篇学术论文，详细介绍了针对特定机器学习模型的新优化方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Marius Willner, Maximilian Scharf, Andr\'e Uschmajew, Timo Felser, Marco Trenti · 2026-09-02 04:00

随机优化的树张量网络

arXiv:2609.00870v1 Announce Type: cross Abstract: Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optimizers for tree tensor networks (TTNs) on both their parameter and quotient manifo…

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随机优化的树张量网络

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