25 citations · 49 across the 8 of their papers we have counts for
10 papers
Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing
Daniel Siegismund, Mario Wieser, Stephan Heyse +1
Uncovering novel drug candidates for treating complex diseases remain one of the most challenging tasks in early discovery research. To tackle this challenge, biopharma research es…
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…
3DMolNet: A Generative Network for Molecular Structures
Vitali Nesterov, Mario Wieser, Volker Roth
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing gen…
Learning Extremal Representations with Deep Archetypal Analysis
Sebastian Mathias Keller, Maxim Samarin, Fabricio Arend Torres +2
Archetypes are typical population representatives in an extremal sense, where typicality is understood as the most extreme manifestation of a trait or feature. In linear feature sp…
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…
Deep Archetypal Analysis
Sebastian Mathias Keller, Maxim Samarin, Mario Wieser +1
"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes. The p…
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…
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Sonali Parbhoo, Mario Wieser, Volker Roth
Estimating the causal effects of an intervention in the presence of confounding is a frequently occurring problem in applications such as medicine. The task is challenging since th…
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…