Before revision: RNNs are popular dynamical models, used for processing sequential data  . Prior theoretical work in understanding the properties of RNNs has focused on models with additive interactions , where the input to a unit is a weighted sum of the output of the remaining units in network .
After revision: RNNs are popular dynamical models, widely used in machine learning (ML) for processing sequential data . Prior theoretical work in understanding the properties of RNNs has focused on models with additive interactions , where the input to a unit is a weighted sum of the output of the remaining units in network .