%0 Generic %1 rubanova2019latent %A Rubanova, Yulia %A Chen, Ricky T. Q. %A Duvenaud, David %D 2019 %K from:adulny neural-ode time-series %T Latent ODEs for Irregularly-Sampled Time Series %U http://arxiv.org/abs/1907.03907 %X Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs), a model we call ODE-RNNs. Furthermore, we use ODE-RNNs to replace the recognition network of the recently-proposed Latent ODE model. Both ODE-RNNs and Latent ODEs can naturally handle arbitrary time gaps between observations, and can explicitly model the probability of observation times using Poisson processes. We show experimentally that these ODE-based models outperform their RNN-based counterparts on irregularly-sampled data.

@misc{rubanova2019latent, abstract = {Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs), a model we call ODE-RNNs. Furthermore, we use ODE-RNNs to replace the recognition network of the recently-proposed Latent ODE model. Both ODE-RNNs and Latent ODEs can naturally handle arbitrary time gaps between observations, and can explicitly model the probability of observation times using Poisson processes. We show experimentally that these ODE-based models outperform their RNN-based counterparts on irregularly-sampled data.}, added-at = {2021-03-26T11:57:54.000+0100}, author = {Rubanova, Yulia and Chen, Ricky T. Q. and Duvenaud, David}, biburl = {https://www.bibsonomy.org/bibtex/29e45ef7da6d425aabca7d6748e7f860b/adulny}, description = {[1907.03907] Latent ODEs for Irregularly-Sampled Time Series}, interhash = {ffede83aeb26698e8253dc1d9471afc4}, intrahash = {9e45ef7da6d425aabca7d6748e7f860b}, keywords = {from:adulny neural-ode time-series}, note = {cite arxiv:1907.03907}, timestamp = {2021-03-26T11:57:54.000+0100}, title = {Latent ODEs for Irregularly-Sampled Time Series}, url = {http://arxiv.org/abs/1907.03907}, year = 2019 }