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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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CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW
Вестник КазНУ. Серия математика, механика, информатика, 2021 Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova +3 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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Performance analysis of different DCNN models in remote sensing image object detection
EURASIP Journal on Image and Video Processing, 2022 In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu +3 more
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Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks
Sensors, 2021 Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently.
Ke Zang, Wenqi Wu, Wei Luo
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Parallel orthogonal deep neural network
Neural Networks, 2021 Ensemble learning methods combine multiple models to improve performance by exploiting their diversity. The success of these approaches relies heavily on the dissimilarity of the base models forming the ensemble. This diversity can be achieved in many ways, with well-known examples including bagging and boosting.
Peyman Sheikholharam Mashhadi +2 more
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StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
IEEE Access, 2016 Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data.
Mohammad Javad Shafiee +2 more
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Adversarial Robustness of Deep Convolutional Neural Network-based Image Recognition Models: A Review
Leida xuebao, 2021 Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change ...
Hao SUN +4 more
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Stress detection using deep neural networks
BMC Medical Informatics and Decision Making, 2020 Background Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing.
Russell Li, Zhandong Liu
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Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed
Energy Reports, 2022 Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has received growing attention from energy ...
Hao Chen, Reidar Staupe-Delgado
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