论文标题
签名：在CPU和GPU上的签名和对数符号变换的可区分计算
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
论文作者
论文摘要
签名者是一个用于计算和执行与签名和对数符号变换有关的功能的库。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，这是这些操作具有GPU能力的第一个库。签名者实现了以前的库中无法使用的新功能，例如有效的预录策略。此外，引入了几种新颖的算法改进，即使在CPU上也没有并行性，也会产生实质性的现实加速。该库是C ++周围的Python包装器，与Pytorch生态系统兼容。它可以通过\ texttt {pip}直接安装。可以在\ texttt {\ url {https://github.com/patrick-kidger/signatory}}上找到源代码，文档，示例，基准和测试。许可证是Apache-2.0。
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.
