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- W3104616229 abstract "Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets." @default.
- W3104616229 created "2020-11-23" @default.
- W3104616229 creator A5040864166 @default.
- W3104616229 date "2020-11-15" @default.
- W3104616229 modified "2023-09-27" @default.
- W3104616229 title "Discovering long term dependencies in noisy time series data using deep learning." @default.
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