3.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University Hospital Schleswig-HolsteinDE21 papers
- Universität HamburgDE14 papers
- Ludwig-Maximilians-Universität MünchenDE11 papers
- University of OxfordGB10 papers
- Hamburg University of TechnologyDE9 papers
- University Medical Center Hamburg-EppendorfDE9 papers
- Worcester Polytechnic InstituteUS9 papers
- Christian-Albrechts-Universität zu KielDE8 papers
- Heidelberg UniversityDE8 papers
- University of KaiserslauternDE8 papers
- Fraunhofer Institute for Digital MedicineDE7 papers
- Institute of Applied Mathematics and MechanicsUA7 papers
11 papers · 1 filter
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6
Federated Learning (FL) holds the potential to advance equality in health by enabling diverse institutions to collaboratively train deep learning (DL) models, even with limited dat…
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…
SKID RAW: Skill Discovery from Raw Trajectories
Daniel Tanneberg, Kai Ploeger, Elmar Rueckert +1
Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching…
FreezeNet: Full Performance by Reduced Storage Costs
Paul Wimmer, Jens Mehnert, Alexandru Condurache
Pruning generates sparse networks by setting parameters to zero. In this work we improve one-shot pruning methods, applied before training, without adding any additional storage co…
Recovering Causal Structures from Low-Order Conditional Independencies
Marcel Wienöbst, Maciej Liśkiewicz
One of the common obstacles for learning causal models from data is that high-order conditional independence (CI) relationships between random variables are difficult to estimate.…
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +4
Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteri…
GraN: An Efficient Gradient-Norm Based Detector for Adversarial and Misclassified Examples
Julia Lust, Alexandru Paul Condurache
Deep neural networks (DNNs) are vulnerable to adversarial examples and other data perturbations. Especially in safety critical applications of DNNs, it is therefore crucial to dete…
Improving GANs for Speech Enhancement
Huy Phan, Ian V. McLoughlin, Lam Pham +4
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use…
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase
Jan Graßhoff, Alexandra Jankowski, Philipp Rostalski
The application of Gaussian processes (GPs) to large data sets is limited due to heavy memory and computational requirements. A variety of methods has been proposed to enable scala…
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Huy Phan, Oliver Y. Chén, Philipp Koch +4
Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a…
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
Adrian Šošić, Elmar Rueckert, Jan Peters +2
Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behav…