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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.11648 |
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| _version_ | 1866916588533317632 |
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| author | McCallum, Sam Foster, James |
| author_facet | McCallum, Sam Foster, James |
| contents | Training Neural ODEs requires backpropagating through an ODE solve. The state-of-the-art backpropagation method is recursive checkpointing that balances recomputation with memory cost. Here, we introduce a class of algebraically reversible ODE solvers that significantly improve upon both the time and memory cost of recursive checkpointing. The reversible solvers presented calculate exact gradients, are high-order and numerically stable -- strictly improving on previous reversible architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11648 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient, Accurate and Stable Gradients for Neural ODEs McCallum, Sam Foster, James Machine Learning Training Neural ODEs requires backpropagating through an ODE solve. The state-of-the-art backpropagation method is recursive checkpointing that balances recomputation with memory cost. Here, we introduce a class of algebraically reversible ODE solvers that significantly improve upon both the time and memory cost of recursive checkpointing. The reversible solvers presented calculate exact gradients, are high-order and numerically stable -- strictly improving on previous reversible architectures. |
| title | Efficient, Accurate and Stable Gradients for Neural ODEs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.11648 |