Springe direkt zu Inhalt

Machine Learning Publications

Gurevich P., Stuke H.
Robustness Against Outliers For Deep Neural Networks By Gradient Conjugate Priors
Preprint:  arXiv:1905.08464 [stat.ML]
(pdf)

Yu He.
Representation learning for ball nut assembly signals by
time delayed embeddings, variational autoencoders and
mutual information maximization.
Masters Thesis, 2018.

Stuke H., Stuke H., Weilenhammer V. A., and Schmack K.
Accounting for noise via lapse rate improves accuracy of parameter estimates of behavioral models in simulated and real-world data 
Submitted
Gurevich P., Stuke H. 
Gradient conjugate priors and deep neural networks.
J. Artificial Intelligence. 10.1016/j.artint.2019.103184
(pdf)

Gurevich P., Stuke H.
Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty.
Neurocomputing. Neurocomputing. 10.1016/j.neucom.2019.03.031
(pdf)
Stuke H., Stuke H., Weilenhammer V. A., and Schmack K. 
Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.
PLoS Computational Biology, Vol. 13 (2017)
(pdf) 
Gurevich P., Stuke H., Kastrup A., Stuke H. and Hildebrandt H. 
Neuropsychological testing and machine learning distinguish Alzheimer's disease from other causes for cognitive impairment. 
Front. Aging Neurosci. 9:114 (2017) 
(pdf)
Klein J., Barbieri S., Stuke H., Bauer M., Egger J., Nimsky C., and Hahn H. K.
On the Reliability of Diffusion Neuroimaging.
Neuroimaging pages 1-24, ISBN 978-953-307-127-5, Sciyo (2010)
(pdf) 
Klein J., Stuke H., Rexilius J., Stieltjes B., Hahn H. K., and Peitgen H.-O. 
Towards User-Independent DTI Quantification. 
SPIE Medical Imaging (Image Processing), Vol. 6914 (2008)
(pdf)