336 citations · 595 across the 19 of their papers we have counts for
3 papers · 1 filter
Understanding Priors in Bayesian Neural Networks at the Unit Level
Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo +1
We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities. Bayesian neural networks with Gaussian priors are well known to in…
DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model
Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda +1
In this paper, we address the problem of how to robustly train a ConvNet for regression, or deep robust regression. Traditionally, deep regression employs the L2 loss function, kno…
A Comprehensive Analysis of Deep Regression
Stéphane Lathuilière, Pablo Mesejo, Xavier Alameda-Pineda +1
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pos…