no code implementations • 28 Dec 2022 • Srinivas Anumasa, Geetakrishnasai Gunapati, P. K. Srijith
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.
no code implementations • 23 Dec 2021 • Srinivas Anumasa, P. K. Srijith
As $T$ implicitly defines the depth of a NODE, posterior distribution over $T$ would also help in model selection in NODE.
no code implementations • 23 Dec 2021 • Srinivas Anumasa, P. K. Srijith
As $T$ implicitly defines the depth of a NODE, posterior distribution over $T$ would also help in model selection in NODE.
no code implementations • WIT (ACL) 2022 • Maunika Tamire, Srinivas Anumasa, P. K. Srijith
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.
no code implementations • 12 Dec 2020 • Srinivas Anumasa, P. K. Srijith
Neural ordinary differential equations (NODEs) treat computation of intermediate feature vectors as trajectories of ordinary differential equation parameterized by a neural network.