25 citations · 110 across the 15 of their papers we have counts for
17 papers
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…
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…
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Mike Wu, Sonali Parbhoo, Michael C. Hughes +2
Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large…
Regional Tree Regularization for Interpretability in Black Box Models
Mike Wu, Sonali Parbhoo, Michael Hughes +5
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resembl…
Tensor B-Spline Numerical Methods for PDEs: a High-Performance Alternative to FEM
Dmytro Shulga, Oleksii Morozov, Volker Roth +2
Tensor B-spline methods are a high-performance alternative to solve partial differential equations (PDEs). This paper gives an overview on the principles of Tensor B-spline methodo…
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…
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…
Probabilistic Clustering of Time-Evolving Distance Data
Julia E. Vogt, Marius Kloft, Stefan Stark +4
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the in…
Copula Mixture Model for Dependency-seeking Clustering
Melanie Rey, Volker Roth
We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the g…
A Complete Analysis of the l_1,p Group-Lasso
Julia Vogt, Volker Roth
The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,\infty} version have been studied in detail and efficient…