Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting [PDF]
International Joint Conference on Artificial Intelligence, 2017 Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Ting Yu, Haoteng Yin, Zhanxing Zhu
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Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [PDF]
Computer Vision and Pattern Recognition, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Wenzhe Shi +7 more
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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs).
Xiangyu Zhang +3 more
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EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces [PDF]
Journal of Neural Engineering, 2016 Objective. Brain–computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI
Vernon J. Lawhern +5 more
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes [PDF]
Robotics: Science and Systems, 2017 Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work,
Yu Xiang +3 more
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Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm
IEEE Access, 2020 As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections.
Youhui Tian
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Speech Command Recognition using Artificial Neural Networks
JOIV: International Journal on Informatics Visualization, 2020 Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is
Sushan Poudel, Dr. R Anuradha
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Forecast Model of TV Show Rating Based on Convolutional Neural Network
Complexity, 2021 The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating
Lingfeng Wang
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A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
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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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