Efficient, Accurate and Stable Gradients for Neural ODEs

Sam McCallum; James Foster

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arXiv · 2410.11648

Efficient, Accurate and Stable Gradients for Neural ODEs

Sam McCallum·James Foster

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Abstract

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.

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Sam McCallum, James Foster. 2024-10-15. Efficient, Accurate and Stable Gradients for Neural ODEs. https://arxiv.org/abs/2410.11648

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