Results 21 to 30 of about 7,083,147 (335)
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
doaj +1 more source
Fusion Recurrent Neural Network
CoRR, 2020 Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will there be a better RNN in the future? In this work, we propose a novel, succinct and promising RNN - Fusion Recurrent Neural Network (Fusion RNN).
Yiwen Sun +5 more
openaire +3 more sources
Frontiers in Human Neuroscience, 2021 It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram (EEG) signals, it was hard to classify the EEG signals and to ...
Qingguo Ma +7 more
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Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns.
Shuai Li +4 more
semanticscholar +1 more source
CAAI Transactions on Intelligence Technology, 2023 Manipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators.
Zhan Li, Shuai Li
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3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 In this article, we propose an alternating directional 3-D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge—structural spatiospectral correlation and global correlation along ...
Kaixuan Wei, Ying Fu, Hua Huang
semanticscholar +1 more source
Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning
Emergency Medicine International, 2022 With the rapid development of society and economy, people’s living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge.
Manman Sun, Lijun Wang
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An attractor-based complexity measurement for Boolean recurrent neural networks. [PDF]
PLoS ONE, 2014 We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics.
Jérémie Cabessa, Alessandro E P Villa
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Quasi-Recurrent Neural Networks
CoRR, 2016 Submitted to conference track at ICLR ...
BRADBURY JAMES +3 more
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SORN : a self-organizing recurrent neural network [PDF]
, 2009 Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing ...
Pipa, Gordon +5 more
core +1 more source