25 citations · 60 across the 12 of their papers we have counts for
12 papers
Data-Free Dynamic Compression of CNNs for Tractable Efficiency
Lukas Meiner, Jens Mehnert, Alexandru Paul Condurache
To reduce the computational cost of convolutional neural networks (CNNs) on resource-constrained devices, structured pruning approaches have shown promise in lowering floating-poin…
The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review
Steffen Hagedorn, Marcel Hallgarten, Martin Stoll +1
Automated driving has the potential to revolutionize personal, public, and freight mobility. Beside accurately perceiving the environment, automated vehicles must plan a safe, comf…
GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations
Julia Lust, Alexandru P. Condurache
Deep neural networks tend to make overconfident predictions and often require additional detectors for misclassifications, particularly for safety-critical applications. Existing d…
Deep Neural Networks with Efficient Guaranteed Invariances
Matthias Rath, Alexandru Paul Condurache
We address the problem of improving the performance and in particular the sample complexity of deep neural networks by enforcing and guaranteeing invariances to symmetry transforma…
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…