Results 21 to 30 of about 911,793 (268)
Research on Real-Time Face Recognition Algorithm Based on Lightweight Network
Jisuanji kexue yu tansuo, 2020 In order to achieve high-precision real-time face recognition on embedded and mobile devices, the advant-ages and disadvantages of common networks in face recognition are analyzed, and an efficient deep convolution neural network model Lightfacenet is ...
ZHANG Dian, WANG Haitao, JIANG Ying, CHEN Xing
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Dual-Precision Deep Neural Network [PDF]
Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition, 2020 On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training.
Park, Jae Hyun +2 more
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A survey of efficient deep neural network
Dianxin kexue, 2020 Recently,deep neural network (DNN) has achieved great success in the field of AI such as computer vision and natural language processing.Thanks to a deeper and larger network structure,DNN’s performance is rapidly increasing.However,deeper and lager deep
Rui MIN
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A greenhouse modeling and control using deep neural networks
Applied Artificial Intelligence, 2021 Deep learning approaches have attracted a lot of interest and competition in a variety of fields. The major goal is to design an effective deep learning process in automatic modeling and control field.
Latifa Belhaj Salah, Fathi Fourati
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Adam Optimization Algorithm for Wide and Deep Neural Network
Knowledge Engineering and Data Science, 2019 The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning.
Imran Khan Mohd Jais +2 more
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Training deep quantum neural networks [PDF]
Nature Communications, 2020 AbstractNeural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal ...
Beer, Kerstin +6 more
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A research on underwater target recognition neural network for small samples
Xibei Gongye Daxue Xuebao, 2022 In the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering.
WU Yanchen, WANG Yingmin
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Continuously Constructive Deep Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 Traditionally, deep learning algorithms update the network weights, whereas the network architecture is chosen manually using a process of trial and error. In this paper, we propose two novel approaches that automatically update the network structure while also learning its weights.
Ozan Irsoy, Ethem Alpaydin
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Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
Applied Sciences, 2020 Reliable geology prediction is of great importance in ensuring the stability and safety of tunnels and other underground engineering projects. This paper presents basic neural network and deep neural network models using a genetic algorithm (GA) to ...
Yuwei Fang +4 more
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Probabilistic Models with Deep Neural Networks [PDF]
Entropy, 2021 Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible.
Andrés R. Masegosa +4 more
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