School of Science, Department of Computer Science,Aalto University, Espoo, Finland,QPR Software Plc, Helsinki, Finland;
School of Science, Department of Computer Science,Aalto University, Espoo, Finland,QPR Software Plc, Helsinki, Finland;
School of Science, Department of Computer Science,Aalto University, Espoo, Finland,HUT Helsinki Institute for Information Technology, Espoo, Finland;
School of Science, Department of Computer Science,Aalto University, Espoo, Finland;
Process mining; Prediction; Classification; Machine learning; Deep learning; Recurrent neural networks; Long Short-Term Memory; Gated Recurrent Unit; Natural Language Processing;
机译:可视化和“诊断分类器”揭示了递归和递归神经网络如何处理层次结构
机译:可视化和“诊断分类器”揭示了复发性和递归神经网络流程分层结构
机译:通过时间算法进行反向传播以使用可变长实例训练循环神经网络
机译:使用经常性神经网络进行分类过程实例
机译:利用递归卷积神经网络对P300 BCI信号进行分类
机译:结合卷积神经网络和递归神经网络进行图像处理找到等离激元结构的光学性质
机译:可视化和“诊断分类器”揭示了复发性和递归神经网络的过程层次结构(扩展摘要)