204 citations · 609 across the 30 of their papers we have counts for
6 papers · 1 filter
exploRNN: Understanding Recurrent Neural Networks through Visual Exploration
Alex Bäuerle, Patrick Albus, Raphael Störk +2
Due to the success of deep learning (DL) and its growing job market, students and researchers from many areas are interested in learning about DL technologies. Visualization has pr…
Deep Volumetric Ambient Occlusion
Dominik Engel, Timo Ropinski
We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) a…
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
Pedro Hermosilla, Marco Schäfer, Matěj Lang +6
Proteins perform a large variety of functions in living organisms, thus playing a key role in biology. As of now, available learning algorithms to process protein data do not consi…
An Environment for Sustainable Research Software in Germany and Beyond: Current State, Open Challenges, and Call for Action
Hartwig Anzt, Felix Bach, Stephan Druskat +40
Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes…
Enabling Viewpoint Learning through Dynamic Label Generation
Michael Schelling, Pedro Hermosilla, Pere-Pau Vazquez +1
Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on c…
Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski +2
This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space -- the neural weight space. To explor…