Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE

  title={Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE},
  author={Mansura Habiba and Barak A. Pearlmutter},
  journal={2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)},
  • M. Habiba, Barak A. Pearlmutter
  • Published 30 October 2021
  • Computer Science
  • 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)
Recent work in deep learning focuses on solving physical systems in the Ordinary Differential Equation or Partial Differential Equation. This current work proposed a variant of Convolutional Neural Networks (CNNs) that can learn the hidden dynamics of a physical system using ordinary differential equation (ODEs) systems (ODEs) and Partial Differential Equation systems (PDEs). Instead of considering the physical system such as image, time -series as a system of multiple layers, this new… 

Figures from this paper



Neural Ordinary Differential Equations

This work shows how to scalably backpropagate through any ODE solver, without access to its internal operations, which allows end-to-end training of ODEs within larger models.

NeuroDiffEq: A Python package for solving differential equations with neural networks

Time integration techniques continue to be an active area of research and include backward difference formulas and Runge-Kutta methods and common spatial discretization approaches include the finite difference method, finite volume method, and finite element method.

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

This work introduces the Phased LSTM model, which extends the L STM unit by adding a new time gate, controlled by a parametrized oscillation with a frequency range which require updates of the memory cell only during a small percentage of the cycle.

Backpropagation Applied to Handwritten Zip Code Recognition

This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.

On illuminations of C2-surfaces in vector graphic description

  • J. Lang
  • Mathematics
    Comput. Graph.
  • 1988