pySigLib -- Fast Signature-Based Computations on CPU and GPU
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918141069623296 |
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| author | Shmelev, Daniil Salvi, Cristopher |
| author_facet | Shmelev, Daniil Salvi, Cristopher |
| contents | Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do not scale to the dataset sizes and sequence lengths encountered in practice. We present pySigLib, a high-performance Python library offering optimised implementations of signatures and signature kernels on CPU and GPU, fully compatible with PyTorch's automatic differentiation. Beyond an efficient software stack for large-scale signature-based computation, we introduce a novel differentiation scheme for signature kernels that delivers accurate gradients at a fraction of the runtime of existing libraries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | pySigLib -- Fast Signature-Based Computations on CPU and GPU Shmelev, Daniil Salvi, Cristopher Machine Learning Mathematical Software 60L10, 65Y05, 68T99 Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do not scale to the dataset sizes and sequence lengths encountered in practice. We present pySigLib, a high-performance Python library offering optimised implementations of signatures and signature kernels on CPU and GPU, fully compatible with PyTorch's automatic differentiation. Beyond an efficient software stack for large-scale signature-based computation, we introduce a novel differentiation scheme for signature kernels that delivers accurate gradients at a fraction of the runtime of existing libraries. |
| title | pySigLib -- Fast Signature-Based Computations on CPU and GPU |
| topic | Machine Learning Mathematical Software 60L10, 65Y05, 68T99 |
| url | https://arxiv.org/abs/2509.10613 |