We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| tgcsaba/ksig | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| check_positive_value | Ran | tgcsaba/ksig/ksig/utils.py code served (permissive licence) · get_code("e80705477bbc1ab1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The signature kernel is a positive definite kernel for sequential and temporal data that has become increasingly popular in machine learning applications due to powerful theoretical guarantees, strong empirical performance, and recently introduced various scalable variations. In this chapter, we give a short introduction to $\texttt{KSig}$, a $\texttt{Scikit-Learn}$ compatible Python package that implements various GPU-accelerated algorithms for computing signature kernels, and performing downstream learning tasks. We also introduce a new algorithm based on tensor sketches which gives strong performance compared to existing algorithms. The package is available at https://github.com/tgcsaba/ksig.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2501.07145")
get_code_for_paper("2501.07145")
have("2501.07145")
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