Nonuniformly sampled data processing using LSTM networks

We investigate classification and regression for nonuniformly sampled variable length sequential data and introduce a novel long short-term memory (LSTM) architecture. In particular, we extend the classical LSTM network with additional time gates, which incorporate the time information as a nonlinear scaling factor on the conventional gates. We also provide forward-pass and backward-pass update equations for the proposed LSTM architecture. We show that our approach is superior to the classical LSTM architecture when there is correlation between time samples. In our experiments, we achieve significant performance gains with respect to the classical LSTM and phased-LSTM architectures. In this sense, the proposed LSTM architecture is highly appealing for the applications involving nonuniformly sampled sequential data. IEEE