Ordinary Differential Equation Long Short-term Memory

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

PaperLearning Long-term Dependencies in Irregularly-Sampled Time Series

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