Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.
2021Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Strodthoff, N.; Wagner, P.; Schaeffter, T.; Samek, W.
Journal Article
2021Generative neural samplers for the quantum Heisenberg chain
Vielhaben, J.; Strodthoff, N.
Journal Article
2021Inferring Respiratory and Circulatory Parameters from Electrical Impedance Tomography With Deep Recurrent Models
Strodthoff, N.; Strodthoff, C.; Becher, T.; Weiler, N.; Frerichs, I.
Journal Article
2020Asymptotically unbiased estimation of physical observables with neural samplers
Nicoli, K.A.; Nakajima, S.; Strodthoff, N.; Samek, W.; Müller, K.-R.; Kessel, P.
Journal Article
2020PTB-XL, a large publicly available electrocardiography dataset
Wagner, P.; Strodthoff, N.; Bousseljot, R.-D.; Kreiseler, D.; Lunze, F.I.; Samek, W.; Schaeffter, T.
Journal Article
2020Towards novel insights in lattice field theory with explainable machine learning
Blücher, S.; Kades, L.; Pawlowski, J.M.; Strodthoff, N.; Urban, J.M.
Journal Article
2020UDSMProt: Universal deep sequence models for protein classification
Strodthoff, N.; Wagner, P.; Wenzel, M.; Samek, W.
Journal Article
2020USMPep: Universal sequence models for major histocompatibility complex binding affinity prediction
Vielhaben, J.; Wenzel, M.; Samek, W.; Strodthoff, N.
Journal Article
2019Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
Laermann, J.; Samek, W.; Strodthoff, N.
Conference Paper
2019Detecting and interpreting myocardial infarction using fully convolutional neural networks
Strodthoff, N.; Strodthoff, C.
Journal Article
2019Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
Strodthoff, N.; Göktepe, B.; Schierl, T.; Hellge, C.; Samek, W.
Journal Article