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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE.

Juntang Zhuang1, Nicha Dvornek1,2, Xiaoxiao Li1

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT USA.

Proceedings of Machine Learning Research
|July 26, 2021
PubMed
Summary
This summary is machine-generated.

Neural Ordinary Differential Equations (NODEs) show improved performance with the new Adaptive Checkpoint Adjoint (ACA) method. ACA enhances gradient accuracy and efficiency, outperforming existing methods in image classification and time-series modeling.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Numerical Analysis

Background:

  • Neural Ordinary Differential Equations (NODEs) offer continuous depth but underperform discrete models.
  • Existing gradient estimation methods like adjoint and naive methods have significant limitations.

Purpose of the Study:

  • To address the performance gap in NODEs by improving gradient estimation.
  • To introduce a novel gradient estimation method for enhanced NODE training.

Main Methods:

  • Proposed the Adaptive Checkpoint Adjoint (ACA) method for automatic differentiation.
  • ACA utilizes trajectory checkpointing for accuracy and shallow computation graphs.
  • ACA supports adaptive solvers and integrates physical knowledge.

Main Results:

  • ACA achieved half the error rate and training time on image classification tasks compared to adjoint and naive methods.
  • NODEs trained with ACA surpassed ResNet in accuracy and reliability.
  • ACA demonstrated superior performance in time-series modeling and physical system simulations.

Conclusions:

  • The ACA method significantly enhances the accuracy and efficiency of NODEs.
  • ACA enables NODEs to achieve state-of-the-art performance on benchmark tasks.
  • ACA offers a robust solution for gradient estimation in continuous-depth models.