Results 31 to 40 of about 876,297 (223)
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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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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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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A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study
Sakarya University Journal of Computer and Information Sciences, 2020 Deep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical ...
Abdullah Talha Kabakuş
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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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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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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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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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