Zobrazeno 1 - 10
of 39
pro vyhledávání: '"ElSaid, Abdelrahman A."'
More than six million people died of the COVID-19 by April 2022. The heavy casualties have put people on great and urgent alert and people try to find all kinds of information to keep them from being inflected by the coronavirus. This research tries
Externí odkaz:
http://arxiv.org/abs/2402.19280
Autor:
Elsaid, Abdelrahman
Crafting neural network architectures manually is a formidable challenge often leading to suboptimal and inefficient structures. The pursuit of the perfect neural configuration is a complex task, prompting the need for a metaheuristic approach such a
Externí odkaz:
http://arxiv.org/abs/2401.17480
Publikováno v:
j.asoc.2023.110737
Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO). CANTS utilizes a continuous search space to indirectly-encode a n
Externí odkaz:
http://arxiv.org/abs/2305.06715
Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or activating a cooling mechanism when temperature thresholds are surpassed
Externí odkaz:
http://arxiv.org/abs/2204.10308
This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), which utilizes synthetic ants that move over a continuous search space bas
Externí odkaz:
http://arxiv.org/abs/2011.10831
Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suffer from vanishing and exploding gradients. In neuroevolution, where ev
Externí odkaz:
http://arxiv.org/abs/2009.09644
Autor:
ElSaid, AbdElRahman, Karns, Joshua, Ororbia II, Alexander, Krutz, Daniel, Lyu, Zimeng, Desell, Travis
Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset. While this has been shown to be quite powerful, its use has generally been restricted by architectural co
Externí odkaz:
http://arxiv.org/abs/2006.02655
Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is through the use of islands, which are also popular in improving performance and convergence of dist
Externí odkaz:
http://arxiv.org/abs/2005.07376
Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, we propose a novel neuro-evolution algorithm based on ant colony optimization (ACO
Externí odkaz:
http://arxiv.org/abs/1909.11849
Neuro-evolution and neural architecture search algorithms have gained increasing interest due to the challenges involved in designing optimal artificial neural networks (ANNs). While these algorithms have been shown to possess the potential to outper
Externí odkaz:
http://arxiv.org/abs/1909.09502