Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function.  first pretrain the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then,  perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales ll with the size of the training set due to the avoidance of kernel matrix inversion. Moreover,  present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.
['Wen-bing Huang', 'Deli Zhao', 'Fuchun Sun', 'Huaping Liu', 'Edward Y. Chang']
I am writing a research paper on a idea Scalable Gaussian process regression using deep neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Scalable Gaussian process regression using deep neural networks"}, {"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 scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function.  first pretrain the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then,  perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales ll with the size of the training set due to the avoidance of kernel matrix inversion. Moreover,  present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression."}]