Results 21 to 30 of about 250,191 (314)
Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network
International Journal of Distributed Sensor Networks, 2022 In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life
Tao Li +5 more
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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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Artificial Neural Networks and Evolutionary Computation in Remote Sensing [PDF]
, 2021 Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images.
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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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Compressed CNN Plant Leaf Ｒecognition Model Fused with Bayesian
Journal of Harbin University of Science and Technology, 2021 Aiming at the problem that there are many parameters in the process of plant leaf recognition and it is easy to produce over-fitting，in order to reduce the cost of storage and calculation，this paper proposes a plant leaf recognition convolutional ...
YAN Ming, ZHU Liang-kuan, JING Wei-peng
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Orthogonal Convolutional Neural Networks [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix ...
Jiayun Wang +3 more
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Voronoi Convolutional Neural Networks
CoRR, 2020 Technical ...
Soroosh Yazdani, Andrea Tagliasacchi
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Offline Handwritten Chinese Character Recognition Based on DBN and CNN Fusion Model
Journal of Harbin University of Science and Technology, 2020 Aiming at the problem that some offline handwritten Chinese characters are similar in shape and it is difficult to extract the feature of characters and the recognition is not accurate, a convolutional neural network and deep belief network fusion model ...
LI Lanying, ZHOU Zhigang, CHEN Deyun
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A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction [PDF]
, 2021 We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data.
Yejian, Wu +3 more
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