TIBIA
发表于 2025-3-21 16:18:48
书目名称Advances in Neural Networks – ISNN 2018影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0149170<br><br> <br><br>书目名称Advances in Neural Networks – ISNN 2018读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0149170<br><br> <br><br>
栖息地
发表于 2025-3-21 20:50:15
Coordinating Plans of Autonomous Agentson neural network. The capsule network uses vector as input and output and dynamic routing updates parameters, which has better effect than convolution neural network. In this paper, a new activation function is proposed for the capsule network and the least weight loss is added to the loss function
不透明
发表于 2025-3-22 03:23:56
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粘土
发表于 2025-3-22 05:52:37
Actions and plans in multiagent domains,chy of ensembles of Hopfield network representing definite classes of objects. Patterns in each of a network are considered as in some sense identical representatives of given class. These networks generate their self-reproducible descendants which can exchange patterns with each other and generate
enormous
发表于 2025-3-22 12:48:10
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Abduct
发表于 2025-3-22 14:05:47
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Ruptured-Disk
发表于 2025-3-22 18:28:49
M. Ravikanth,T. K. Chandrashekarcal Artificial Neural Networks. Although there has been a wide range of research to improve the accuracy of SNNs, their performance is determined not only by accuracy, but also by speed and energy efficiency. In this study, we analyzed the relationship between hyperparameters, accuracy, speed and en
Herbivorous
发表于 2025-3-22 23:27:03
M. Ravikanth,T. K. Chandrashekaraining data to reduce the design cost and enable applying cross-modal networks in sparse data environments. Two approaches for building X-CNNs are presented. The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data. The iterativ
杀菌剂
发表于 2025-3-23 04:27:31
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Glower
发表于 2025-3-23 08:16:18
C. L. Rollinson,E. W. Rosenbloomhitectures to perform better than shallow ones. This paper introduces complex-valued deep belief networks, which can be used for unsupervised pretraining of complex-valued deep neural networks. Experiments on the MNIST dataset using different network architectures show better results of the complex-