Hypersolvers: Toward Fast Continuous-Depth Models

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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.
Publisher
The Neural Information Processing Systems
Issue Date
2020-12-09
Language
English
Citation

34th Conference on Neural Information Processing Systems, NeurIPS 2020

URI
http://hdl.handle.net/10203/279062
Appears in Collection
IE-Conference Papers(학술회의논문)
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