Search Results for author: Mario Wieser

Found 10 papers, 4 papers with code

3DMolNet: A Generative Network for Molecular Structures

no code implementations8 Oct 2020 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.

Quantization Translation

Inverse Learning of Symmetries

1 code implementation NeurIPS 2020 Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth

Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.

Learning Extremal Representations with Deep Archetypal Analysis

1 code implementation3 Feb 2020 Sebastian Mathias Keller, Maxim Samarin, Fabricio Arend Torres, Mario Wieser, Volker Roth

The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information.

Band Gap

Deep Archetypal Analysis

1 code implementation30 Jan 2019 Sebastian Mathias Keller, Maxim Samarin, Mario Wieser, Volker Roth

"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes.

Representation Learning

Estimating Causal Effects With Partial Covariates For Clinical Interpretability

no code implementations26 Nov 2018 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.

Causal Inference

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

no code implementations19 Nov 2018 Adam Kortylewski, Mario Wieser, Andreas Morel-Forster, Aleksander Wieczorek, Sonali Parbhoo, Volker Roth, Thomas Vetter

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, texture, and shape of objects.

Bayesian Inference

Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates

no code implementations6 Jul 2018 Sonali Parbhoo, Mario Wieser, Aleksander Wieczorek, Volker Roth

Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.

Causal Inference

Greedy Structure Learning of Hierarchical Compositional Models

no code implementations CVPR 2019 Adam Kortylewski, Aleksander Wieczorek, Mario Wieser, Clemens Blumer, Sonali Parbhoo, Andreas Morel-Forster, Volker Roth, Thomas Vetter

In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.

Transfer Learning

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