Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Barak A Pearlmutter"'
Publikováno v:
PLoS ONE, Vol 8, Iss 9, p e73456 (2013)
Parkinsonian and essential tremor can often be effectively treated by deep brain stimulation. We propose a novel explanation for the mechanism by which this technique ameliorates tremor: a reduction of the delay in the relevant motor control loops vi
Externí odkaz:
https://doaj.org/article/81a720d7948442d5840914ee166d57fd
Autor:
Vince D Calhoun, Vamsi K Potluru, Ronald Phlypo, Rogers F Silva, Barak A Pearlmutter, Arvind Caprihan, Sergey M Plis, Tülay Adalı
Publikováno v:
PLoS ONE, Vol 8, Iss 8, p e73309 (2013)
A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. T
Externí odkaz:
https://doaj.org/article/8247747505dc4ff28bdeb51588fe1656
Autor:
Mansura Habiba, Barak A. Pearlmutter
Publikováno v:
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 o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3396d72c12dd8c79c198fc099ba36838
https://doi.org/10.1109/icecet52533.2021.9698766
https://doi.org/10.1109/icecet52533.2021.9698766
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bb46fd33e64018a36b7264e53f5f4a1
http://arxiv.org/abs/2111.00314
http://arxiv.org/abs/2111.00314
Autor:
Mansura Habiba, Barak A. Pearlmutter
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc5119020e9c09b7313c99cb78a58081
http://arxiv.org/abs/2111.00326
http://arxiv.org/abs/2111.00326
Publikováno v:
2021 32nd Irish Signals and Systems Conference (ISSC).
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:
https://explore.openaire.eu/search/publication?articleId=doi_________::19941352f4eaa37fd768a446d3f042c6
https://doi.org/10.1109/issc52156.2021.9467884
https://doi.org/10.1109/issc52156.2021.9467884
Autor:
Barak A. Pearlmutter, Mansura Habiba
Publikováno v:
2020 31st Irish Signals and Systems Conference (ISSC).
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:
https://explore.openaire.eu/search/publication?articleId=doi_________::667768febc5cf241c97c0a729ec45efd
https://doi.org/10.1109/issc49989.2020.9180216
https://doi.org/10.1109/issc49989.2020.9180216
Autor:
Barak A. Pearlmutter, Mansura Habiba
Publikováno v:
2020 31st Irish Signals and Systems Conference (ISSC).
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fdb036761a632c532214bb9031069a3
https://doi.org/10.1109/issc49989.2020.9180182
https://doi.org/10.1109/issc49989.2020.9180182
Autor:
Adil Reghai, Sébastien Geeraert, Barak A. Pearlmutter, Olivier Pironneau, Charles-Albert Lehalle
Publikováno v:
ESAIM: Proceedings
ESAIM: Proceedings, 2017
ESAIM: Proceedings, EDP Sciences, 2017
ESAIM: Proceedings and Surveys, Vol 59, Pp 56-75 (2017)
ESAIM: Proceedings, 2017
ESAIM: Proceedings, EDP Sciences, 2017
ESAIM: Proceedings and Surveys, Vol 59, Pp 56-75 (2017)
Automatic differentiation has been involved for long in applied mathematics as an alternative to finite difference to improve the accuracy of numerical computation of derivatives. Each time a numerical minimization is involved, automatic differentiat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6cf3be1524c918ce4ac58cb57879f2c6
https://doi.org/10.1051/proc/201759056
https://doi.org/10.1051/proc/201759056