Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Habiba, Mansura"'
Continuous-time series is essential for different modern application areas, e.g. healthcare, automobile, energy, finance, Internet of things (IoT) and other related areas. Different application needs to process as well as analyse a massive amount of
Externí odkaz:
http://arxiv.org/abs/2409.09106
Autor:
Habiba, Mansura, Pearlmutter, Barak A.
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 o
Externí odkaz:
http://arxiv.org/abs/2111.00343
Autor:
Habiba, Mansura, Pearlmutter, Barak A.
This work proposes a Neural Network model that can control its depth using an iterate-to-fixed-point operator. The architecture starts with a standard layered Network but with added connections from current later to earlier layers, along with a gate
Externí odkaz:
http://arxiv.org/abs/2111.00326
Continuous medical time series data such as ECG is one of the most complex time series due to its dynamic and high dimensional characteristics. In addition, due to its sensitive nature, privacy concerns and legal restrictions, it is often even comple
Externí odkaz:
http://arxiv.org/abs/2111.00314
There is an analogy between the ResNet (Residual Network) architecture for deep neural networks and an Euler solver for an ODE. The transformation performed by each layer resembles an Euler step in solving an ODE. We consider the Heun Method, which i
Externí odkaz:
http://arxiv.org/abs/2105.06168
Autor:
Habiba, Mansura, Pearlmutter, Barak A.
Informative missingness is unavoidable in the digital processing of continuous time series, where the value for one or more observations at different time points are missing. Such missing observations are one of the major limitations of time series p
Externí odkaz:
http://arxiv.org/abs/2005.10693
Autor:
Habiba, Mansura, Pearlmutter, Barak A.
Neural differential equations are a promising new member in the neural network family. They show the potential of differential equations for time series data analysis. In this paper, the strength of the ordinary differential equation (ODE) is explore
Externí odkaz:
http://arxiv.org/abs/2005.09807
Publikováno v:
In Computers and Electrical Engineering May 2023 108
