tn4ml让张量网络轻松融入机器学习流程，支持定制化训练。
tn4ml: Tensor Network Training and Customization for Machine Learning
- 提供数据嵌入、目标函数定义、优化策略等模块化设计
- 在表格与图像数据上实现监督与无监督学习，性能可调
- 适合想尝试张量网络替代神经网络的研究者
张量网络已成为基础科学中机器学习问题的重要替代方案，推动其向实际应用发展。本文提出 tn4ml，一个新设计的库，可无缝集成张量网络至机器学习优化流程。受现有机器学习框架启发，该库提供用户友好的结构，包含数据嵌入、目标函数定义及多种优化策略的模型训练模块。通过两个案例展示其通用性：基于表格数据的监督学习与基于图像数据的无监督学习。此外，分析了对机器学习流程中不同部分进行张量网络定制对性能指标的影响。
原文摘要 · Abstract (English)
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper introduces tn4ml, a novel library designed to seamlessly integrate Tensor Networks into optimization pipelines for Machine Learning tasks. Inspired by existing Machine Learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, and model training using diverse optimization strategies. We demonstrate its versatility through two examples: supervised learning on tabular data and unsupervised learning on an image dataset. Additionally, we analyze how customizing the parts of the Machine Learning pipeline for Tensor Networks influences performance metrics.
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