签名：签名和logsignature变换的可微计算，在CPU和GPU上
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
摘要：
Signatory是一个用于计算和执行与签名和logsignature转换相关的功能的库。重点是机器学习，因此包括CPU并行性，GPU支持和反向传播等功能。据我们所知，这是第一个支持GPU的库。Signatory实现了以前的库中没有的新功能，例如高效的预计算策略。此外，几个新的算法的改进，产生大量的现实世界的加速，即使在CPU上没有并行。该库作为C++的Python包装器运行，并与PyTorch生态系统兼容。可以通过\texttt{pip}直接安装。源代码、文档、示例、基准和测试可在\texttt{\url{https://github.com/patrick-kidger/signatory}}上找到。许可证是Apache-2.0。
英文摘要：
Signatory is a library for calculating and performing functionality related to the signature and logsignature transforms. The focus is on machine learning, and as such includes features such as CPU parallelism, GPU support, and backpropagation. To our knowledge it is the first GPU-capable library for these operations. Signatory implements new features not available in previous libraries, such as efficient precomputation strategies. Furthermore, several novel algorithmic improvements are introduced, producing substantial real-world speedups even on the CPU without parallelism. The library operates as a Python wrapper around C++, and is compatible with the PyTorch ecosystem. It may be installed directly via \texttt{pip}. Source code, documentation, examples, benchmarks and tests may be found at \texttt{\url{https://github.com/patrick-kidger/signatory}}. The license is Apache-2.0.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
