Hypersolvers: Toward Fast Continuous-Depth Models
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Publication:6345382
arXiv2007.09601MaRDI QIDQ6345382
Author name not available (Why is that?)
Publication date: 19 July 2020
Abstract: The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilization in problems of non-trivial size has often proved impossible due to poor computational scalability. This work paves the way for scalable Neural ODEs with time-to-prediction comparable to traditional discrete networks. We introduce hypersolvers, neural networks designed to solve ODEs with low overhead and theoretical guarantees on accuracy. The synergistic combination of hypersolvers and Neural ODEs allows for cheap inference and unlocks a new frontier for practical application of continuous-depth models. Experimental evaluations on standard benchmarks, such as sampling for continuous normalizing flows, reveal consistent pareto efficiency over classical numerical methods.
Has companion code repository: https://github.com/DiffEqML/diffeqml-research/tree/master/hypersolver
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