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Markus Thill
Markus Thill
Precitec GmbH & Co. KG
Verified email at freenet.de - Homepage
Title
Cited by
Cited by
Year
Temporal convolutional autoencoder for unsupervised anomaly detection in time series
M Thill, W Konen, H Wang, T Bäck
Applied Soft Computing 112, 107751, 2021
1052021
Online anomaly detection on the webscope S5 dataset: A comparative study
M Thill, W Konen, T Bäck
Evolving and Adaptive Intelligent Systems (EAIS), 2017, 1-8, 2017
542017
Time series encodings with temporal convolutional networks
M Thill, W Konen, T Bäck
International Conference on Bioinspired Methods and Their Applications, 161-173, 2020
382020
Reinforcement learning with n-tuples on the game Connect-4
M Thill, P Koch, W Konen
Parallel Problem Solving from Nature-PPSN XII: 12th International Conference …, 2012
302012
Online adaptable learning rates for the game Connect-4
S Bagheri, M Thill, P Koch, W Konen
IEEE Transactions on Computational Intelligence and AI in Games 8 (1), 33-42, 2014
282014
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks.
M Thill, S Däubener, W Konen, T Bäck, P Barancikova, M Holena, ...
ITAT, 17-25, 2019
272019
Temporal difference learning with eligibility traces for the game connect four
M Thill, S Bagheri, P Koch, W Konen
2014 IEEE Conference on Computational Intelligence and Games, 1-8, 2014
272014
Time Series Anomaly Detection with Discrete Wavelet Transforms and Maximum Likelihood Estimation
M Thill, W Konen, T Bäck
International Work-Conference on Time Series (ITISE2017) 2, 11-22, 2017
182017
Temporal difference learning methods with automatic step-size adaption for strategic board games: Connect-4 and Dots-and-Boxes
M Thill
Cologne University of Applied Sciences Masters thesis, 2015
132015
MarkusThill/MGAB: The Mackey-Glass Anomaly Benchmark
M Thill, W Konen, T Bäck
Version v1. 0.1. Zenodo. doi 10, 2020
62020
Online adaptable time series anomaly detection with discrete wavelet transforms and multivariate gaussian distributions
M Thill, W Konen, T Bäck
Archives of Data Science, Series A (Online First) 5 (1), 17, 2018
32018
Machine Learning and Deep Learning Approaches for Multivariate Time Series Prediction and Anomaly Detection
M Thill
PhD thesis, University of Leiden, URL https://hdl. handle. net/1887/3279161, 2022
12022
Process signal reconstruction and anomaly detection in laser machining processes
M Krause, M Thill, F Mack
US Patent App. 18/047,859, 2023
2023
Temporal con olutional autoencoder for unsuper ised anomal detection in time series., doi: 10.1016/. asoc. 2021.107751 Version: Publisher's Version License: Licensed under …
M Thill, W Konen, H Wang, THW Bäck
Law (mendment Ta erne) Downloaded from: https://hdl. handle. net/1887/3280042, 2021
2021
Discrete Wavelet Transforms and Multivariate Gaussian Distributions for Anomaly Detection in Time Series
M Thill, W Konen, T Bäck
Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24 …, 2017
2017
Temporal Coherence in TD-Learning for Strategic Board Games
S Bagheri, M Thill
2014
Reinforcement Learning mit N-Tupel-Systemen für Vier Gewinnt
M Thill
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