25 citations · 60 across the 12 of their papers we have counts for
3 papers · 1 filter
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey
Paul Wimmer, Jens Mehnert, Alexandru Paul Condurache
State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costl…
Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs
Paul Wimmer, Jens Mehnert, Alexandru Paul Condurache
Unstructured pruning is well suited to reduce the memory footprint of convolutional neural networks (CNNs), both at training and inference time. CNNs contain parameters arranged in…
Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration
Matthias Rath, Alexandru Paul Condurache
Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. This makes them applicable t…