analiza aiqbench
TorchKM introduces a GPU-accelerated library for kernel machines that leverages efficient matrix operations to significantly speed up training and model selection pipelines. By offering a familiar scikit-learn API, it lowers the barrier for integrating advanced kernel methods like SVMs into scalable AI workflows. This tool provides a practical solution for teams seeking to optimize predictive performance without sacrificing compatibility with existing Python-based development environments.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
arXiv:2606.06742v2 Announce Type: replace Abstract: 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.
Abstract: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 this https URL, and the package can be easily installed via PyPI.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2606.06742 [cs.LG]
(or arXiv:2606.06742v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06742 Focus to learn more arXiv-issued DOI via DataCite
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