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Continuous Depth Recurrent Neural Differential Equations
Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalizes RNN models by continuously evolving the hidden states in both the temporal and depth dimensions.
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations
As $T$ implicitly defines the depth of a NODE, posterior distribution over $T$ would also help in model selection in NODE.
Latent Time Neural Ordinary Differential Equations
As $T$ implicitly defines the depth of a NODE, posterior distribution over $T$ would also help in model selection in NODE.
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification
In this work, we propose to use recurrent neural ordinary differential equations (RNODE) for social media post classification which consider the time of posting and allow the computation of hidden representation to evolve in a time-sensitive continuous manner.
Delay Differential Neural Networks
Neural ordinary differential equations (NODEs) treat computation of intermediate feature vectors as trajectories of ordinary differential equation parameterized by a neural network.
