Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

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书目名称Artificial Neural Networks and Machine Learning – ICANN 2017影响因子(影响力)




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017影响因子(影响力)学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017网络公开度




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017网络公开度学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017被引频次




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017被引频次学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017年度引用




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017年度引用学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017读者反馈




书目名称Artificial Neural Networks and Machine Learning – ICANN 2017读者反馈学科排名

LUCY

Robot Localization and Orientation Detection Based on Place Cells and Head-Direction Cells

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Artificial Neural Networks and Machine Learning – ICANN 201726th International C

WAX

0302-9743 g and Medical Applications; Advances in Machine Learning.. There are 63 short paper abstracts that are included in the back matter of the volume..978-3-319-68599-1978-3-319-68600-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

连词

Kognitiv-physiologischer Forschungsansatzimulation results show that our approach achieves significantly better performance compared with two existing approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency.

Obituary

遗产

https://doi.org/10.1007/978-3-322-91075-2e being compatible with many existing neural architectures. We present the recurrent ladder network, a novel modification of the ladder network, for semi-supervised learning of recurrent neural networks which we evaluate with a phoneme recognition task on the TIMIT corpus. Our results show that the

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尊敬

https://doi.org/10.1007/978-3-322-90299-3w that concurrent action execution and action perception influence each other. We have developed a physiologically-inspired neural model that accounts for the neural encoding of perceived actions and motor plans, and their interactions. The core of the model is a set of coupled neural fields that re

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