37 citations · 99 across the 6 of their papers we have counts for
6 papers
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antorán, David Janz, James Urquhart Allingham +4
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and a…
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Runa Eschenhagen, Erik Daxberger, Philipp Hennig +1
Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. I…
Laplace Redux -- Effortless Bayesian Deep Learning
Erik Daxberger, Agustinus Kristiadi, Alexander Immer +3
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty qu…
Bayesian Deep Learning via Subnetwork Inference
Erik Daxberger, Eric Nalisnick, James Urquhart Allingham +2
The Bayesian paradigm has the potential to solve core issues of deep neural networks such as poor calibration and data inefficiency. Alas, scaling Bayesian inference to large weigh…
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
Austin Tripp, Erik Daxberger, José Miguel Hernández-Lobato
Many important problems in science and engineering, such as drug design, involve optimizing an expensive black-box objective function over a complex, high-dimensional, and structur…
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
Erik Daxberger, José Miguel Hernández-Lobato
Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituti…