Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
Patrick Kidger, Terry J. Lyons
Abstract
Signatory is a library for calculating signature and logsignature transforms and related functionality. The focus is on making this functionality available for use in machine learning, and as such includes features such as GPU support and backpropagation. To our knowledge it is the first publically available GPU-capable library for these operations. It also implements several new algorithmic improvements, and provides several new features not available in previous libraries. The library operates as a Python wrapper around C++, and is compatible with the PyTorch ecosystem. It may be installed directly via pip. Source code, documentation, examples, benchmarks and tests may be found at this https URL. The license is Apache-2.0.
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Cited by top-tier papers11
- Neural Controlled Differential Equations for Irregular Time SeriesPatrick Kidger, James Morrill, James Foster, Terry J. LyonsNeurIPS 2020 · 850 citations
- Neural SDEs as Infinite-Dimensional GANsPatrick Kidger, James Foster, Xuechen Li, Terry J. LyonsICML 2021 · 214 citations
- Neural Rough Differential Equations for Long Time SeriesJames Morrill, Cristopher Salvi, Patrick Kidger, James FosterICML 2021 · 176 citations
- Efficient and Accurate Gradients for Neural SDEsPatrick Kidger, James Foster, Xuechen Li, Terry J. LyonsNeurIPS 2021 · 107 citations
- Framing RNN as a kernel method: A neural ODE approachAdeline Fermanian, Pierre Marion, Jean-Philippe Vert, Gérard BiauNeurIPS 2021 · 34 citations
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