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Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning
2021
Entropy
It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the function model. Recently, it was suggested that, albeit training with marginal likelihood, the deterministic nature of a feature extractor might lead to overfitting, and replacement with a Bayesian network seemed to cure it. Here, we propose the
doi:10.3390/e23111387
pmid:34828085
pmcid:PMC8618322
fatcat:ozm3uarzdnfcfpvncva2omd6c4