Differential Equations for Continuous-Time Deep Learning
Lars Ruthotto
This short, self-contained article seeks to introduce and survey
continuous-time deep learning approaches that are based on neural ordinary
differential equations (neural ODEs). It primarily targets readers familiar
with ordinary and partial differential equations and their analysis who are
curious to see their role in machine learning. Using three examples from
machine learning and applied mathematics, we will see how neural ODEs can
provide new insights into deep learning and a foundation for more efficient
algorithms.