no code implementations • 30 Oct 2021 • Mansura Habiba, Barak A. Pearlmutter
Recent work in deep learning focuses on solving physical systems in the Ordinary Differential Equation or Partial Differential Equation.
no code implementations • 30 Oct 2021 • Mansura Habiba, Barak A. Pearlmutter
In a typical case, where the ``wormhole'' connections are inactive, this is inexpensive; but when they are active, the network takes a longer time to settle down, and the gradient calculation is also more laborious, with an effect similar to making the network deeper.
no code implementations • 30 Oct 2021 • Mansura Habiba, Eoin Brophy, Barak A. Pearlmutter, Tomas Ward
Continuous medical time series data such as ECG is one of the most complex time series due to its dynamic and high dimensional characteristics.
1 code implementation • 13 May 2021 • Mehrdad Maleki, Mansura Habiba, Barak A. Pearlmutter
There is an analogy between the ResNet (Residual Network) architecture for deep neural networks and an Euler solver for an ODE.
no code implementations • 20 May 2020 • Mansura Habiba, Barak A. Pearlmutter
(ii)~can Neural ODEs solve the irregular sampling rate challenge of existing neural network models for a continuous time series, i. e., length and dynamic nature, (iii)~how to reduce the training and evaluation time of existing Neural ODE systems?
no code implementations • 20 May 2020 • Mansura Habiba, Barak A. Pearlmutter
Practical applications, e. g., sensor data, healthcare, weather, generates data that is in truth continuous in time, and informative missingness is a common phenomenon in these datasets.