Erik Daxberger | DeepAI

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∙ 06/17/2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning

The linearised Laplace method for estimating model uncertainty has recei...

0 Javier Antoran, et al. ∙

∙ 11/05/2021

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning

Deep neural networks are prone to overconfident predictions on outliers....

0 Runa Eschenhagen, et al. ∙

∙ 06/28/2021

Laplace Redux – Effortless Bayesian Deep Learning

Bayesian formulations of deep learning have been shown to have compellin...

0 Erik Daxberger, et al. ∙

∙ 10/28/2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference

The Bayesian paradigm has the potential to solve some of the core issues...

0 Erik Daxberger, et al. ∙

∙ 06/16/2020

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Many important problems in science and engineering, such as drug design,...

31 Austin Tripp, et al. ∙

∙ 12/11/2019

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

Despite their successes, deep neural networks still make unreliable pred...

0 Erik Daxberger, et al. ∙

∙ 07/02/2019

Mixed-Variable Bayesian Optimization

The optimization of expensive to evaluate, black-box, mixed-variable fun...

12 Erik Daxberger, et al. ∙

∙ 06/30/2018

Embedding Models for Episodic Memory

In recent years a number of large-scale triple-oriented knowledge graphs...

0 Yunpu Ma, et al. ∙