ODE-LSTMs allow for learning long-term dependencies in irregularly sampled time-series. The motivation arises from the fact that all Neural ODE models provably suffer from the vanishing/exploding gradients. Therefore, they face difficulties in learning long-term dependencies. In our NeurIPS 2020 paper, we proposed ODE-LSTMs, as a powerful time-series modeling framework which can deal with data arriving at arbitrary time-stamp.
Github – Here, is a TensorFlow 2 implementation of a dozen advanced ODE-based RNNs, as well as our performant ODE-LSTMs: ODE-LSTMs