11 citations · 18 across the 5 of their papers we have counts for
6 papers
Voreen -- An Open-source Framework for Interactive Visualization and Processing of Large Volume Data
Dominik Drees, Simon Leistikow, Xiaoyi Jiang +1
Technological advances for measuring or simulating volume data have led to large data sizes in many research areas such as biology, medicine, physics, and geoscience. Here, large d…
ASEVis: Visual Exploration of Active System Ensembles to Define Characteristic Measures
Marina Evers, Raphael Wittkowski, Lars Linsen
Simulation ensembles are a common tool in physics for understanding how a model outcome depends on input parameters. We analyze an active particle system, where each particle can u…
Multi-dimensional parameter-space partitioning of spatio-temporal simulation ensembles
Marina Evers, Lars Linsen
Numerical simulations are commonly used to understand the parameter dependence of given spatio-temporal phenomena. Sampling a multi-dimensional parameter space and running the resp…
Uncertainty-aware Topic Modeling Visualization
Valerie Müller, Christian Sieg, Lars Linsen
Topic modeling is a state-of-the-art technique for analyzing text corpora. It uses a statistical model, most commonly Latent Dirichlet Allocation (LDA), to discover abstract topics…
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks
Karim Huesmann, Luis Garcia Rodriguez, Lars Linsen +1
Overfitting is one of the fundamental challenges when training convolutional neural networks and is usually identified by a diverging training and test loss. The underlying dynamic…
Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization
Karim Huesmann, Soeren Klemm, Lars Linsen +1
Overfitting is one of the most common problems when training deep neural networks on comparatively small datasets. Here, we demonstrate that neural network activation sparsity is a…