Search Results for author:
Found 6 papers, 1 papers with code
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE
Recent work in deep learning focuses on solving physical systems in the Ordinary Differential Equation or Partial Differential Equation.
Neural Network based on Automatic Differentiation Transformation of Numeric Iterate-to-Fixedpoint
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.
ECG synthesis with Neural ODE and GAN models
Continuous medical time series data such as ECG is one of the most complex time series due to its dynamic and high dimensional characteristics.
HeunNet: Extending ResNet using Heun's Methods
There is an analogy between the ResNet (Residual Network) architecture for deep neural networks and an Euler solver for an ODE.
Neural ODEs for Informative Missingness in Multivariate Time Series
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.
Neural Ordinary Differential Equation based Recurrent Neural Network Model
(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?
