Zobrazeno 1 - 10
of 2 834
pro vyhledávání: '"Sideris, P."'
We propose a novel algorithm for combined unit/filter and layer pruning of deep neural networks that functions during training and without requiring a pre-trained network to apply. Our algorithm optimally trades-off learning accuracy and pruning leve
Externí odkaz:
http://arxiv.org/abs/2411.09127
Autor:
Hu, Jin, Sever, Emrah, Babazadeh, Omid, Jeffrey, Ian, Okhmatovski, Vladimir, Sideris, Constantine
An H-matrix accelerated direct solver employing the high-order Chebyshev-based Boundary Integral Equation (CBIE) method has been formulated, tested, and profiled for performance on high contrast dielectric materials and electrically large perfect ele
Externí odkaz:
http://arxiv.org/abs/2408.17116
Autor:
Paul, Jagabandhu, Sideris, Constantine
This article presents an $O(N\log N)$ method for numerical solution of Maxwell's equations for dielectric scatterers using a 3D boundary integral equation (BIE) method. The underlying BIE method used is based on a hybrid Nystr\"{o}m-collocation metho
Externí odkaz:
http://arxiv.org/abs/2408.02274
We propose an algorithm capable of identifying and eliminating irrelevant layers of a neural network during the early stages of training. In contrast to weight or filter-level pruning, layer pruning reduces the harder to parallelize sequential comput
Externí odkaz:
http://arxiv.org/abs/2406.04549
Autor:
Hu, Jin, Sideris, Constantine
This paper introduces an efficient approach for solving the Electric Field Integral Equation (EFIE) with high-order accuracy by explicitly enforcing the continuity of the impressed current densities across boundaries of the surface patch discretizati
Externí odkaz:
http://arxiv.org/abs/2403.04334
Publikováno v:
BioMedInformatics, Vol 4, Iss 3, Pp 2002-2021 (2024)
Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions fr
Externí odkaz:
https://doaj.org/article/9472a012e0564006a7ee31f6a5b18f2d
Publikováno v:
J, Vol 7, Iss 3, Pp 302-318 (2024)
Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in asse
Externí odkaz:
https://doaj.org/article/0647ab6b100a441d80282bdfe7741744
Publikováno v:
Developments in the Built Environment, Vol 20, Iss , Pp 100572- (2024)
In the United States reducing greenhouse gas emissions associated with building construction by 90% by 2050 necessitates significant measures to be undertaken within the construction industry. Such measures include advanced construction techniques an
Externí odkaz:
https://doaj.org/article/c9bde890d1ac4e28aebf11630c96141f
Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The m
Externí odkaz:
http://arxiv.org/abs/2211.01001
Autor:
L. Foresti, B. Puigdomènech Treserras, D. Nerini, A. Atencia, M. Gabella, I. V. Sideris, U. Germann, I. Zawadzki
Publikováno v:
Nonlinear Processes in Geophysics, Vol 31, Pp 259-286 (2024)
Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fie
Externí odkaz:
https://doaj.org/article/b519eb1d2e2a4e828713280eda1c2e8c