9 citations · 28 across the 9 of their papers we have counts for
10 papers · 1 filter
A Bayesian Approach to Invariant Deep Neural Networks
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos +2
We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes. We s…
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…
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer, Matthias Bauer, Vincent Fortuin +2
Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data,…
On Disentanglement in Gaussian Process Variational Autoencoders
Simon Bing, Vincent Fortuin, Gunnar Rätsch
Complex multivariate time series arise in many fields, ranging from computer vision to robotics or medicine. Often we are interested in the independent underlying factors that give…
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…
Factorized Gaussian Process Variational Autoencoders
Metod Jazbec, Michael Pearce, Vincent Fortuin
Variational autoencoders often assume isotropic Gaussian priors and mean-field posteriors, hence do not exploit structure in scenarios where we may expect similarity or consistency…