Moreover, when confined to a model architecture consisting of a single Neural ODE followed by a linear layer, T A-BN achieves 91.1% test

This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer.

Neural ODEs also provide atheoretical framework to study deep learning models from the continuous viewpoint, using the arsenal of ODE theory [40, 25, 41]. Importantly, they can also be seen as the continuous analog of ResNets.

Indeed, the results in Table 1, which shows13 the mean absolute percentage errors (MAPE), demonstrates this. The ac-14 curacy of neural ODE for the Poisson process is on par with our neural15 JSDE. However, for the Hawkes process (Exponential), Hawkes process16 (Power-Law), and self-correcting process, neural ODE gives much larger17 predictions errors. Forthesocial/medicaldatasets,weuseda20/64-24 dimensional latent state and parameterized the functions with two-hidden-layer MLPs with 32/64 hidden units. The time series modeling software that we used is designed for long event sequences and ignores the idle time after31 thelastevent.