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cs.LG2026
An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes
Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-Lobato
DGPs are probabilistic models with remarkable prediction performance that concatenate GPs across several layers. Exact inference in DGPs is intractable, and variational inference i…
cs.LG2024
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning
Luis A. Ortega, Simón Rodríguez-Santana, Daniel Hernández-Lobato
Recently, there has been an increasing interest in performing post-hoc uncertainty estimation about the predictions of pre-trained deep neural networks (DNNs). Given a pre-trained…