24 citations · 31 across the 4 of their papers we have counts for
5 papers · 1 filter
Activation-Space Uncertainty Quantification for Pretrained Networks
Richard Bergna, Stefan Depeweg, Sergio Calvo-Ordoñez +3
Reliable uncertainty estimates are crucial for deploying pretrained models; yet, many strong methods for quantifying uncertainty require retraining, Monte Carlo sampling, or expens…
Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
Richard Bergna, Stefan Depeweg, Sergio Calvo Ordonez +3
Uncertainty quantification in neural networks through methods such as Dropout, Bayesian neural networks and Laplace approximations is either prone to underfitting or computationall…
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
Stefan Depeweg, Constantin A. Rothkopf, Frank Jäkel
More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Althou…
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft +1
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class…
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in th…