Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent variational methods for learning deep generative models,  introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters. Of particular interest is the use of temporal generative models for counterfactual inference.  investigate the efficacy of such models for counterfactual inference, and to that end  introduce the "Healing MNIST" dataset where long-term structure, noise and actions are applied to sequences of digits.  show the efficacy of our method for modeling this dataset.  further show how our model can be used for counterfactual inference for patients, based on electronic health record data of 8,000 patients over 4.5 years.
['Rahul G. Krishnan', 'Uri Shalit', 'David Sontag']
I am writing a research paper on a idea Deep Kalman Filters
[{"from": "human", "value": "I am writing a research paper on a idea Deep Kalman Filters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent variational methods for learning deep generative models,  introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters. Of particular interest is the use of temporal generative models for counterfactual inference.  investigate the efficacy of such models for counterfactual inference, and to that end  introduce the \"Healing MNIST\" dataset where long-term structure, noise and actions are applied to sequences of digits.  show the efficacy of our method for modeling this dataset.  further show how our model can be used for counterfactual inference for patients, based on electronic health record data of 8,000 patients over 4.5 years."}]