说明: MNIST 数据集来自美国国家标准与技术研究所, National Institute of Standards and Technology (NIST). 训练集 (training set) 由来自 250 个不同人手写的数字构成, 其中 50% 是高中学生, 50% 来自人口普查局 (the Census Bureau) 的工作人员. 测试集(test set) 也是同样比例的手写数字数据. Training set images: train-images-idx3-ubyte.gz <qq_39023633> 在 上传 | 大小:11534336
说明: Abstract Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusi <weixin_41245916> 在 上传 | 大小:340992
说明: James Martens JMARTENS @ CS . TORONTO . EDU Ilya Sutskever ILYA @ CS . UTORONTO . CA University of Toronto, Canada Abstract In this work we resolve the long-outstanding problem of how to effectively train recurrent neu- ral networks (RNNs) on comple <weixin_41245916> 在 上传 | 大小:302080
说明: Abstract—This paper studies the approximation ability of con- tinuous-time recurrent neural networksto dynamical time-variant systems. It proves that any finite time trajectory of a given dynam- ical time-variant system can be approximated by the in <weixin_41245916> 在 上传 | 大小:154624