GPU加速的核学习库，让传统核方法跑得更快更高效。
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
- 基于GPU优化线性代数，智能复用矩阵运算提升效率
- 在标准基线上实现显著提速，预测性能保持竞争力
- 兼容scikit-learn接口，适合集成到AI工作流中
TorchKM 是一个开源的核机器学习库，支持支持向量机、核逻辑回归和核分位数回归，并具备GPU加速能力。该库采用类scikit-learn的API设计，充分利用GPU友好的线性代数运算，通过智能复用矩阵操作，加速完整的训练与模型选择流程。基准测试显示，其预测性能与标准基线相当，但速度大幅提升。高效的可编程架构使其可作为AI驱动工作流中的核学习组件。代码与文档见 https://github.com/YikaiZhang95/torchkm，可通过PyPI便捷安装。
原文摘要 · Abstract (English)
TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The library features a scikit-learn-style API and is designed to exploit GPU-friendly linear algebra, accelerating the full training and model-selection pipeline through intelligent reuse of matrix operations. Benchmarks show competitive predictive performance with substantial speedups over standard baselines. The efficiency and programmable design also make TorchKM a kernel-learning component for AI-driven workflows. Code and documentation are available at https://github.com/YikaiZhang95/torchkm, and the package can be easily installed via PyPI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。