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| Main Author: | |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.14394 |
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| _version_ | 1866914719291408384 |
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| author | Liu, Yucong |
| author_facet | Liu, Yucong |
| contents | In this study, we explore the integration of Neural Networks, a powerful class of functions known for their exceptional approximation capabilities. Our primary emphasis is on the integration of multi-layer Neural Networks, a challenging task within this domain. To tackle this challenge, we introduce a novel numerical method that consist of a forward algorithm and a corrective procedure. Our experimental results demonstrate the accuracy achieved through our integration approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_14394 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neural Networks are Integrable Liu, Yucong Numerical Analysis In this study, we explore the integration of Neural Networks, a powerful class of functions known for their exceptional approximation capabilities. Our primary emphasis is on the integration of multi-layer Neural Networks, a challenging task within this domain. To tackle this challenge, we introduce a novel numerical method that consist of a forward algorithm and a corrective procedure. Our experimental results demonstrate the accuracy achieved through our integration approach. |
| title | Neural Networks are Integrable |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2310.14394 |