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Keywords = nerual networks

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16 pages, 443 KiB  
Article
Entropy-Enhanced Attention Model for Explanation Recommendation
by Yongjie Yan, Guang Yu and Xiangbin Yan
Entropy 2022, 24(4), 535; https://doi.org/10.3390/e24040535 - 11 Apr 2022
Cited by 3 | Viewed by 3734
Abstract
Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also unable to capture [...] Read more.
Most of the existing recommendation systems using deep learning are based on the method of RNN (Recurrent Neural Network). However, due to some inherent defects of RNN, recommendation systems based on RNN are not only very time consuming but also unable to capture the long-range dependencies between user comments. Through the sentiment analysis of user comments, we can better capture the characteristics of user interest. Information entropy can reduce the adverse impact of noise words on the construction of user interests. Information entropy is used to analyze the user information content and filter out users with low information entropy to achieve the purpose of filtering noise data. A self-attention recommendation model based on entropy regularization is proposed to analyze the emotional polarity of the data set. Specifically, to model the mixed interactions from user comments, a multi-head self-attention network is introduced. The loss function of the model is used to realize the interpretability of recommendation systems. The experiment results show that our model outperforms the baseline methods in terms of MAP (Mean Average Precision) and NDCG (Normalized Discounted Cumulative Gain) on several datasets, and it achieves good interpretability. Full article
(This article belongs to the Topic Machine and Deep Learning)
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13 pages, 1832 KiB  
Letter
Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor
by Yu Wu, Youming Guo, Hua Bao and Changhui Rao
Sensors 2020, 20(17), 4877; https://doi.org/10.3390/s20174877 - 28 Aug 2020
Cited by 42 | Viewed by 4332
Abstract
We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of [...] Read more.
We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of the focal and defocused intensity images. When compared to the traditional phase diversity (PD) algorithms, the PD-CNN is a light-weight model without complicated iterative transformation and optimization process. Experiments have been done to demonstrate the accuracy and speed of the proposed approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications)
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