E-LSTM: An extension to the LSTM architecture for incorporating long lag dependencies

The Long Short-Term Memory (LSTM) architecture is one of the most successful types of Recurrent Neural Networks (RNNs). However, the number of parameters that LSTMs need to achieve acceptable performance might be larger than desired for standard devices. In this work, an Extended LSTM (E-LSTM) architecture is proposed to reduce the number of parameters needed to achieve similar performance to LSTMs. The architecture of the proposed E-LSTM is …