Popular approaches for quantifying predictive uncertainty in deep neural networks often
involve distributions over weights or multiple models, for instance via Markov Chain …

Bayesian deep learning (BDL) is a promising approach to achieve well-calibrated
predictions on distribution-shifted data. Nevertheless, there exists no large-scale survey that …

We introduce a deterministic variational formulation for training Bayesian last layer neural
networks. This yields a sampling-free, single-pass model and loss that effectively improves …

Bayesian neural networks often approximate the weight-posterior with a Gaussian
distribution. However, practical posteriors are often, even locally, highly non-Gaussian, and …

Conducting experiments and gathering data for machine learning models is a complex and
expensive endeavor, particularly when confronted with limited information. Typically …

Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial
limitation: they fail to maintain invariance under reparameterization, ie BNNs assign different …

Graphs serve as generic tools to encode the underlying relational structure of data. Often
this graph is not given, and so the task of inferring it from nodal observations becomes …

Biological neural networks effortlessly tackle complex computational problems and excel at
predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired …

Conducting experiments and gathering data for machine learning models is a complex and
expensive endeavor, particularly when confronted with limited information. Typically …

Recently, deep learning techniques are gradually replacing traditional statistical and
machine learning models as the first choice for price forecasting tasks. In this paper, we …