10 citations · 11 across the 6 of their papers we have counts for
6 papers
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…
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
The Linearized Laplace Approximation (LLA) has been recently used to perform uncertainty estimation on the predictions of pre-trained deep neural networks (DNNs). However, its wide…
Correcting Model Bias with Sparse Implicit Processes
Simón Rodríguez Santana, Luis A. Ortega, Daniel Hernández-Lobato +1
Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure. Model choice may impose strong biases on the resulting predictions, which can hinder…
Deep Variational Implicit Processes
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
Implicit processes (IPs) are a generalization of Gaussian processes (GPs). IPs may lack a closed-form expression but are easy to sample from. Examples include, among others, Bayesi…
Function-space Inference with Sparse Implicit Processes
Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato
Implicit Processes (IPs) represent a flexible framework that can be used to describe a wide variety of models, from Bayesian neural networks, neural samplers and data generators to…
Adversarial -divergence Minimization for Bayesian Approximate Inference
Simón Rodríguez Santana, Daniel Hernández-Lobato
Neural networks are popular state-of-the-art models for many different tasks.They are often trained via back-propagation to find a value of the weights that correctly predicts the…