3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.GR2025
Palace: A Library for Interactive GPU-Accelerated Large Tensor Processing and Visualization
Dominik Drees, Benjamin Risse
Tensor datasets (two-, three-, or higher-dimensional) are fundamental to many scientific fields utilizing imaging or simulation technologies. Advances in these methods have led to…
cs.LG2021
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…
cs.LG2020★ 3 cited
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…