Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1. To this end  show how a bi-linear model of transformations, such as a gated autoencoder, can be turned into a recurrent network, by training it to predict future frames from the current one and the inferred transformation using backprop-through-time.  also show how stacking multiple layers of gating units in a recurrent pyramid makes it possible to represent the "syntax" of complicated time series, and that it can outperform standard recurrent neural networks in terms of prediction accuracy on a variety of tasks.
['Vincent Michalski', 'Roland Memisevic', 'Kishore Reddy Konda']
I am writing a research paper on a idea Modeling Deep Temporal Dependencies with Recurrent Grammar Cells
[{"from": "human", "value": "I am writing a research paper on a idea Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose modeling time series by representing the transformations that take a frame at time t to a frame at time t+1. To this end  show how a bi-linear model of transformations, such as a gated autoencoder, can be turned into a recurrent network, by training it to predict future frames from the current one and the inferred transformation using backprop-through-time.  also show how stacking multiple layers of gating units in a recurrent pyramid makes it possible to represent the \"syntax\" of complicated time series, and that it can outperform standard recurrent neural networks in terms of prediction accuracy on a variety of tasks."}]