10 citations · 11 across the 2 of their papers we have counts for
12 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…