Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm  show that the iterates will converge to samples from the true posterior distribution as  anneal the stepsize. This seamless transition beten optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting.  also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a “sampling threshold” and collects samples after it has been surpassed.  apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.
['Max Welling', 'Yee Whye Teh']
I am writing a research paper on a idea Bayesian Learning via Stochastic Gradient Langevin Dynamics
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian Learning via Stochastic Gradient Langevin Dynamics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:   propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm  show that the iterates will converge to samples from the true posterior distribution as  anneal the stepsize. This seamless transition beten optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting.  also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a \u201csampling threshold\u201d and collects samples after it has been surpassed.  apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients."}]