Heavy Ball Neural Ordinary Differential Equations

Bao Wang, Andrea L. Bertozzi, Stanley J. Osher, Tan M. Nguyen, Vai Suliafu, Hangjie Ji, Hedi Xia

Publication date: 10 October 2021

Abstract: We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural ODEs (NODEs) training and inference. HBNODEs have two properties that imply practical advantages over NODEs: (i) The adjoint state of an HBNODE also satisfies an HBNODE, accelerating both forward and backward ODE solvers, thus significantly reducing the number of function evaluations (NFEs) and improving the utility of the trained models. (ii) The spectrum of HBNODEs is well structured, enabling effective learning of long-term dependencies from complex sequential data. We verify the advantages of HBNODEs over NODEs on benchmark tasks, including image classification, learning complex dynamics, and sequential modeling. Our method requires remarkably fewer forward and backward NFEs, is more accurate, and learns long-term dependencies more effectively than the other ODE-based neural network models. Code is available at url{https://github.com/hedixia/HeavyBallNODE}.

Has companion code repository: https://github.com/hedixia/heavyballnode

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

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