13 citations · 36 across the 8 of their papers we have counts for
8 papers
Learning Conditional Invariance through Cycle Consistency
Maxim Samarin, Vitali Nesterov, Mario Wieser +3
Identifying meaningful and independent factors of variation in a dataset is a challenging learning task frequently addressed by means of deep latent variable models. This task can…
Inverse Learning of Symmetries
Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek +1
Symmetry transformations induce invariances which are frequently described with deep latent variable models. In many complex domains, such as the chemical space, invariances can be…
On the Difference Between the Information Bottleneck and the Deep Information Bottleneck
Aleksander Wieczorek, Volker Roth
Combining the Information Bottleneck model with deep learning by replacing mutual information terms with deep neural nets has proved successful in areas ranging from generative mod…
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Adam Kortylewski, Mario Wieser, Andreas Morel-Forster +4
Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, tex…
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates
Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek +1
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding. The task is often complicated by the fact…
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Aleksander Wieczorek, Mario Wieser, Damian Murezzan +1
Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a m…
Causal Compression
Aleksander Wieczorek, Volker Roth
We propose a new method of discovering causal relationships in temporal data based on the notion of causal compression. To this end, we adopt the Pearlian graph setting and the dir…
Bayesian Markov Blanket Estimation
Dinu Kaufmann, Sonali Parbhoo, Aleksander Wieczorek +3
This paper considers a Bayesian view for estimating a sub-network in a Markov random field. The sub-network corresponds to the Markov blanket of a set of query variables, where the…