Results 21 to 30 of about 298,507 (214)
Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks
Electronic Research Archive, 2023 Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets ...
Xite Yang +4 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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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 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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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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Deep Sequential Neural Network
, 2014 Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer, one mapping among these candidates is selected according to a sequential decision process.
Denoyer, Ludovic, Gallinari, Patrick
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Comparisons of different deep learning-based methods on fault diagnosis for geared system
International Journal of Distributed Sensor Networks, 2019 The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system ...
Bing Han +3 more
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