25 citations · 110 across the 15 of their papers we have counts for
5 papers · 1 filter
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…