Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How can  efficiently propagate uncertainty in a latent state representation with recurrent neural networks?  introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model’s posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model,  improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling.
['Marco Fraccaro', 'Søren Kaae Sønderby', 'Ulrich Paquet', 'Ole Winther']
I am writing a research paper on a idea Sequential Neural Models with Stochastic Layers
[{"from": "human", "value": "I am writing a research paper on a idea Sequential Neural Models with Stochastic Layers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: How can  efficiently propagate uncertainty in a latent state representation with recurrent neural networks?  introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model\u2019s posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over the uncertainty in a latent path, like a state space model,  improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling."}]