Hypersolvers: Toward Fast Continuous-Depth Models
This work addresses the scalability problem for researchers and practitioners using continuous-depth models, enabling practical applications, though it is incremental as it builds on the existing Neural ODE paradigm.
The paper tackles the poor computational scalability of Neural ODEs by introducing hypersolvers, neural networks designed to solve ODEs with low overhead, resulting in time-to-prediction comparable to traditional discrete networks and consistent Pareto efficiency over classical numerical methods on benchmarks like continuous normalizing flows.
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.