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Roman Rybka, Yury Davydov, Danila Vlasov, Alexey Serenko, Alexander Sboev and Vyacheslav Ilyin
Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local...
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Tushar Ganguli and Edwin K. P. Chong
We present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model...
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Artemiy Belousov, Ivan Kisel, Robin Lakos and Akhil Mithran
Algorithms optimized for high-performance computing, which ensure both speed and accuracy, are crucial for real-time data analysis in heavy-ion physics experiments. The application of neural networks and other machine learning methodologies, which are fa...
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Aleksandr Alekseev, Leonid Kozhemyakin, Vladislav Nikitin and Julia Bolshakova
This paper aimed to increase accuracy of an Alzheimer?s disease diagnosing function that was obtained in a previous study devoted to application of decision roots to the diagnosis of Alzheimer?s disease. The obtained decision root is a discrete switching...
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Ebenezer O. Oluwasakin and Abdul Q. M. Khaliq
Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and m...
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Hong-Hua Huang, Jian-Fei Luo, Feng Gan and Philip K. Hopke
Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attenti...
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Chin-Yi Chen and Jih-Jeng Huang
Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking al...
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Wen-Chang Cheng, Hung-Chou Hsiao, Yung-Fa Huang and Li-Hua Li
This research proposes a single network model architecture for mask face recognition using the FaceNet training method. Three pre-trained convolutional neural networks of different sizes are combined, namely InceptionResNetV2, InceptionV3, and MobileNetV...
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Giseok Jeong, Kookjin Kim, Sukjoon Yoon, Dongkyoo Shin and Jiwon Kang
As the world undergoes rapid digitalization, individuals and objects are becoming more extensively connected through the advancement of Internet networks. This phenomenon has been observed in governmental and military domains as well, accompanied by a ri...
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Weijun Pan, Peiyuan Jiang, Zhuang Wang, Yukun Li and Zhenlong Liao
In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we c...
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