Schwerpunkte/Kompetenzen
- Machine Learning, Deep Learning
- Bayessche und Chow-Liu-Netze
- Neuronale Netze
- Statistische Lerntheorie
Publikationen
Highlightpublikationen
- Gramsch, S.; Sarishvili, A.; Schmeißer, A.:
Analysis of the Fiber Laydown Quality in Spunbond Processes with Simulation Experiments Evaluated by Blocked Neural Networks
In: Advances in Polymer Technology Vol 3 (2020).
- Sarishvili, A.; Winter, J.; Luhmann, H. J.; Mildenberger, E.:
Probabilistic graphical model identifies clusters of EEG patterns in recordings from neonates.
In: Clinical Neurophysiology 130, Pages 1342-1350 (2019).
- Sarishvili, A.; Wirsen, A.; Jirstrand, M.:
On Chow-Liu forest based regularization of deep belief networks.
28th International Conference on Artificial Neural Networks and Machine Learning ICANN 2019: Workshop and Special Sessions.
In: Lecture Notes in Computer Science (LNCS, volume 11731) Pages 353-364 (2019).
- Sarishvili, A.; Hanselmann, G.:
Software Reliability prediction via two different implementations of Bayesian model averaging.
ECML/PKDD 2013, European conference on machine learning and principles and practice of knowledge discovery in databases.
In: Workshop Proceedings COPEM2013: Solving complex machine learning problems with ensamble methods. Prag (2013). - Sarishvili, A.; Andersson, Ch.; Franke, J.; Kroisandt, G.:
On the Consistency of the Blocked Neural Network Estimator in Time Series Analysis.
In: Neural Computation 18, Volume 10, Pages 1-14 (2006).