A Tree Architecture of LSTM Networks for Sequential Regression with Missing Data

22 May 2020 S. Onur Sahin Suleyman S. Kozat

We investigate regression for variable length sequential data containing missing samples and introduce a novel tree architecture based on the Long Short-Term Memory (LSTM) networks. In our architecture, we employ a variable number of LSTM networks, which use only the existing inputs in the sequence, in a tree-like architecture without any statistical assumptions or imputations on the missing data, unlike all the previous approaches... (read more)

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