Before revision:  Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we address the gradients by leveraging the adjoint sensitivity framework to develop a DMFT for the gradients. The theory developed here sheds light on the rich dynamical behaviour produced by gating interactions and has implications for architectural choices and learning dynamics .
After revision:  Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we address the gradients by leveraging the adjoint sensitivity framework from control theory in the DMFT. This paves the way for the use of powerful field-theoretic techniques to study training/gradients in large RNNs .