An Application of the Holonomic Gradient Method to the Neural Tangent Kernel
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913568522240000 |
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| author | Sakoda, Akihiro Takayama, Nobuki |
| author_facet | Sakoda, Akihiro Takayama, Nobuki |
| contents | A holonomic system of linear partial differential equations is, roughly speaking, a system whose solution space is finite dimensional. A distribution that is a solution of a holonomic system is called a holonomic distribution. We give methods to numerically evaluate dual activations of holonomic activator distributions for neural tangent kernels. These methods are based on computer algebra algorithms for rings of differential operators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Application of the Holonomic Gradient Method to the Neural Tangent Kernel Sakoda, Akihiro Takayama, Nobuki Machine Learning A holonomic system of linear partial differential equations is, roughly speaking, a system whose solution space is finite dimensional. A distribution that is a solution of a holonomic system is called a holonomic distribution. We give methods to numerically evaluate dual activations of holonomic activator distributions for neural tangent kernels. These methods are based on computer algebra algorithms for rings of differential operators. |
| title | An Application of the Holonomic Gradient Method to the Neural Tangent Kernel |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.23626 |