Results 21 to 30 of about 876,297 (223)

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, Jianxi Yang, Cen Chen, Joey Tianyi Zhou, Yi Pan, Zeng Zeng   +5 more
doaj   +1 more source

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, Shaurya Goyal, Rishabh Gupta   +2 more
doaj   +1 more source

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, Choi, Ji Sub, Ko, Jong Hwan   +2 more
openaire   +2 more sources

Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition

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, Vanni Galli, Sören Bellenberg, Olga Ilie, Malte Herold, Stephan Christel, Mariia Boretska, Igor V. Pivkin, Paul Wilmes, Wolfgang Sand, Mario Vera, Mark Dopson   +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, Charles K. Chui, Shao-Bo Lin, Shao-Bo Lin, Ding-Xuan Zhou   +4 more
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Frequency Response Mode Prediction of Power System After Large Disturbances Based on Deep Belief Neural Network

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, Zhiwei Wang, Xin Liu, Wujing Li, Qiufang Li, Yagang Zhang, Qianchang Chen, Shuyu Guo, Zhi Xu   +8 more
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$Π-$nets: Deep Polynomial Neural Networks

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020
Accepted in CVPR ...
Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, Stefanos Zafeiriou   +5 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
doaj   +1 more source

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, Piotr Przystałka, Ryszard Wiaderkiewicz, Grzegorz Mrugacz   +3 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, B. S. Omarov, O. A. Postolache, M. Zh. Sakypbekova   +3 more
doaj   +1 more source