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Varsha S. Lalapura, Veerender Reddy Bhimavarapu, J. Amudha and Hariram Selvamurugan Satheesh
The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local o...
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Dennis Papenfuß, Bennet Gerlach, Stefan Fischer and Mohamed Ahmed Hail
The IoT encompasses objects, sensors, and everyday items not typically considered computers. IoT devices are subject to severe energy, memory, and computation power constraints. Employing NDN for the IoT is a recent approach to accommodate these issues. ...
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Haipeng Lin, Xuefeng Song, Fei Dai, Fengwei Zhang, Qiang Xie and Huhu Chen
Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we dev...
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Chi Gao, Xiaofei Xu, Zhizou Yang, Liwei Lin and Jian Li
In recent decades, memory-intensive applications have experienced a boom, e.g., machine learning, natural language processing (NLP), and big data analytics. Such applications often experience out-of-memory (OOM) errors, which cause unexpected processes t...
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Carmelo Scribano, Danilo Pezzi, Giorgia Franchini and Marco Prato
With the recent advancements in the field of diffusion generative models, it has been shown that defining the generative process in the latent space of a powerful pretrained autoencoder can offer substantial advantages. This approach, by abstracting away...
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Zhuo Li, Hengyi Li and Lin Meng
Currently, with the rapid development of deep learning, deep neural networks (DNNs) have been widely applied in various computer vision tasks. However, in the pursuit of performance, advanced DNN models have become more complex, which has led to a large ...
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Bin Li, Feng Tong, Xiujing Gao, Junhui Yao, Yuehai Zhou and Hongwu Huang
With its superiorities of low cost, high flexibility and deployment convenience, small-size autonomous underwater vehicles (AUVs) have been extensively applied to perform a variety of undersea missions. While underwater acoustic (UWA) communication provi...
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Ziyi Li, Yang Li, Yanping Wang, Guangda Xie, Hongquan Qu and Zhuoyang Lyu
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resource co...
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Shih Yu Chang, Hsiao-Chun Wu and Yifan Wang
In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a la...
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Viktar Atliha and Dmitrij ?e?ok
Image captioning is a very important task, which is on the edge between natural language processing (NLP) and computer vision (CV). The current quality of the captioning models allows them to be used for practical tasks, but they require both large compu...
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