We lifted 6 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| patrick-kidger/signatory | canonical | 2 of 3 |
| copy not recorded | — | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| interpret_basepoint | Ran | this paper's copy was not recorded; identical code first harvested from patrick-kidger/signatory pointer only · get_code("05d34e6628f185c5") |
| interpret_basepoint | Ran | patrick-kidger/signatory/src/signatory/signature_module.py code served (permissive licence) · get_code("b52cc0b8f9401c89") |
| interpret_initial | Ran | this paper's copy was not recorded; identical code first harvested from patrick-kidger/signatory pointer only · get_code("c1154b1a79258c50") |
| interpret_initial | Ran | patrick-kidger/signatory/src/signatory/signature_module.py code served (permissive licence) · get_code("fadf8c1518d20fc6") |
| signature | Not yet run | this paper's copy was not recorded; identical code first harvested from patrick-kidger/signatory pointer only · get_code("b7f24a317d8a0910") |
| signature | Not yet run | patrick-kidger/signatory/src/signatory/signature_module.py code served (permissive licence) · get_code("a5e4773dbfdff294") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We provide an introduction to the signature method, focusing on its theoretical properties and machine learning applications. Our presentation is divided into two parts. In the first part, we present the definition and fundamental properties of the signature of a path. The signature is a sequence of numbers associated with a path that captures many of its important analytic and geometric properties. As a sequence of numbers, the signature serves as a compact description (dimension reduction) of a path. In presenting its theoretical properties, we assume only familiarity with classical real analysis and integration, and supplement theory with straightforward examples. We also mention several advanced topics, including the role of the signature in rough path theory. In the second part, we present practical applications of the signature to the area of machine learning. The signature method is a non-parametric way of transforming data into a set of features that can be used in machine learning tasks. In this method, data are converted into multi-dimensional paths, by means of embedding algorithms, of which the signature is then computed. We describe this pipeline in detail, making a link with the properties of the signature presented in the first part. We furthermore review some of the developments of the signature method in machine learning and, as an illustrative example, present a detailed application of the method to handwritten digit classification.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1603.03788")
get_code_for_paper("1603.03788")
have("1603.03788")
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