565 citations · 695 across the 18 of their papers we have counts for
6 papers · 1 filter
Data augmentation in Bayesian neural networks and the cold posterior effect
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso +3
Bayesian neural networks that incorporate data augmentation implicitly use a ``randomly perturbed log-likelihood [which] does not have a clean interpretation as a valid likelihood…
BNNpriors: A library for Bayesian neural network inference with different prior distributions
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk +1
Bayesian neural networks have shown great promise in many applications where calibrated uncertainty estimates are crucial and can often also lead to a higher predictive performance…
Exact Langevin Dynamics with Stochastic Gradients
Adrià Garriga-Alonso, Vincent Fortuin
Stochastic gradient Markov Chain Monte Carlo algorithms are popular samplers for approximate inference, but they are generally biased. We show that many recent versions of these me…
Bayesian Neural Network Priors Revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober +5
Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs ab…
Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks
Adrià Garriga-Alonso, Mark van der Wilk
Infinite width limits of deep neural networks often have tractable forms. They have been used to analyse the behaviour of finite networks, as well as being useful methods in their…
Understanding Variational Inference in Function-Space
David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso +1
Recent work has attempted to directly approximate the `function-space' or predictive posterior distribution of Bayesian models, without approximating the posterior distribution ove…