Results 21 to 30 of about 806,463 (274)
Enhanced gradient learning for deep neural networks
IET Image Processing, 2022 Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach
Ming Yan +5 more
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Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction
IEEE Access, 2021 Time series prediction with neural networks has been the focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in using deep learning models for time series prediction, and hence it is ...
Rohitash Chandra +2 more
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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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Biotechnology Reports, 2019 Background: Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases.
Antoine Buetti-Dinh +11 more
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Deep Net Tree Structure for Balance of Capacity and Approximation Ability
Frontiers in Applied Mathematics and Statistics, 2019 Deep learning has been successfully used in various applications including image classification, natural language processing and game theory. The heart of deep learning is to adopt deep neural networks (deep nets for short) with certain structures to ...
Charles K. Chui +4 more
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IEEE Access, 2023 The power system frequency is an important indicator that reflects the power system’s operating status. Through real-time detection or prediction, it can effectively ensure stable power system operation.
Wenzhuo Wang +8 more
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An Interactive Visualization for Feature Localization in Deep Neural Networks
Frontiers in Artificial Intelligence, 2020 Deep artificial neural networks have become the go-to method for many machine learning tasks. In the field of computer vision, deep convolutional neural networks achieve state-of-the-art performance for tasks such as classification, object detection, or ...
Martin Zurowietz, Tim W. Nattkemper
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Semantic segmentation of human oocyte images using deep neural networks
BioMedical Engineering OnLine, 2021 Background Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest ...
Anna Targosz +3 more
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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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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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