7. Phased LSTM Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences 瑞士的文章 NIPS 2016 亮点1: 怎么把时间轴塞入 LSTM-cell 中. s 是相位偏移, 使得同一个时间轴可以在不同的 LSTM-cell 中错开, 相当于有"平行时间轴&quot...
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences 永2013 Love & life10 人赞同了该文章 这是一篇对lstm进行改进的论文,主要成果是可以解决当下lstm无法处理不规则输入序列的问题. 现有的lstm模型里面有三个gate,通过对三个gate的学习,可以学习到序列上的pattern.但是现有的模...
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (NIPS 2016) - Tensorflow 1.0 - philipperemy/tensorflow-phased-lstm
10. The input matrix first goes through the LSTM layer and the dropout process. Then, the result passes through another LSTM layer, and in the process of dropping out, the work enters the dense layer. The dropout layer is the traditional forgot gate of the LSTM. This literature proposes ...
Both methods use new techniques involving spatial convolutions in temporal recurrent iterations such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRU) ones. The core of CLM3D is a stack of convLTSM2D layers, each of which is applied to a single altitude. CGRU3D uses a ...
Depending on the advantages of TCN in depth temporal feature extraction and Bi-LSTM in global time series feature extraction, the typical working mode of phased array radar is accurately recognized. The experimental results show that under the condition of complex parameter interleaving, the ...
Subsequently, a Bayesian long short-term memory (BayesLSTM) architecture is developed and integrated into the proposed framework for estimating the remaining useful life (RUL) of critical devices/subsystems. The effectiveness of the proposed deep learning-based prognostic framework is eva...
Finally, we train the recognition model with MLSTM-FCN neural network. The simulation results show that the recognition rate is above 92% at a low signal-to-noise ratio (SNR) of 7dB.Zhou, YangHarbin Engineering UniversityDeng, Zhian