Temporal convolutional autoencoder for unsupervised anomaly detection in time series M Thill, W Konen, H Wang, T Bäck Applied Soft Computing 112, 107751, 2021 | 173 | 2021 |
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 | 57 | 2017 |
Time series encodings with temporal convolutional networks M Thill, W Konen, T Bäck International Conference on Bioinspired Methods and Their Applications, 161-173, 2020 | 53 | 2020 |
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 | 33 | 2012 |
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 | 32 | 2017 |
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 | 30 | 2019 |
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 | 29 | 2014 |
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 | 27 | 2014 |
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 | 13 | 2015 |
MarkusThill/MGAB: The Mackey-Glass Anomaly Benchmark M Thill, W Konen, T Bäck Version v1. 0.1. Zenodo. doi 10, 2020 | 11 | 2020 |
MarkusThill/MGAB: The Mackey-Glass Anomaly Benchmark. Version v1. 0.1 M Thill, W Konen, T Bäck Zenodo, 2020 | 5 | 2020 |
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 | 5 | 2018 |
Process signal reconstruction and anomaly detection in laser machining processes M Krause, M Thill, F Mack US Patent App. 18/047,859, 2023 | 2 | 2023 |
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 | 2 | 2022 |
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 | | |