13 citations · 19 across the 4 of their papers we have counts for
4 papers
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe +1
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation pr…
A Review of Change of Variable Formulas for Generative Modeling
Ullrich Köthe
Change-of-variables (CoV) formulas allow to reduce complicated probability densities to simpler ones by a learned transformation with tractable Jacobian determinant. They are thus…
Training Invertible Neural Networks as Autoencoders
The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe
Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. In this work,…
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Malte Tölle, Ullrich Köthe, Florian André +2
Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to…