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Titlebook: Neural Information Processing; 25th International C Long Cheng,Andrew Chi Sing Leung,Seiichi Ozawa Conference proceedings 2018 Springer Nat

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Improved Kernel Density Estimation Self-organizing Incremental Neural Network to Perform Big Data Annformation. The AKDESOINN can adapt itself to the changing data properties by estimating the probability density function. Further, the experimental results depict that AKDESOINN succeeds in maintaining the performance of KDESOINN, while depicting an ability to adapt to the changing data.
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Multi-scale Feature Decode and Fuse Model with CRF Layer for Boundary DetectionF-RNN layer to reduce the number of irrelevant features and eliminate the weak correlation information while retaining the important structural attributes. Extensive experiments on BSDS500 [.] dataset and the larger NYUD [.] dataset show that the effectiveness of the proposed model and of the overall hierarchical framework.
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Network of Recurrent Neural Networks: Design for Emergencee introduce an information-theoretical framework to quantify the information dynamics in recurrent neural structures. And the evaluation results show that several NOR models achieve similar or better emergent information processing capabilities compared with LSTM.
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0302-9743 s different domains. The second volume, LNCS 11302, is organized in topical sections on other neural network models, stability analysis, optimization, and supervised learning..978-3-030-04178-6978-3-030-04179-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
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