Titlebook: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track; European Conference, Yuxiao Dong,Nicolas Kourtellis,Jose

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Sophie van den Berg,Marwan Hassaniactice; (2) to offer them strategies for minimizing the potential for their being named in a lawsuit; and (3) to provide guidance for the management of current and emerging situations. The book discusses the da978-1-4419-2468-1978-0-387-72175-0

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Anna Nguyen,Franz Krause,Daniel Hagenmayer,Michael Färberactice; (2) to offer them strategies for minimizing the potential for their being named in a lawsuit; and (3) to provide guidance for the management of current and emerging situations. The book discusses the da978-1-4419-2468-1978-0-387-72175-0

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Methods for Automatic Machine-Learning Workflow Analysismance prediction. Another interesting application is the suggestion of component types, for which a classification baseline is presented. A slightly adapted GCN using both graph- and node-level information further improves upon this baseline. The used codebase as well as all experimental setups with

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DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Networking. We provide a comparison with three data-driven approaches as well as multi-task fine-tuning in predicting the PBL vertical profiles outputted by the Yonsei University (YSU) parameterization in the Weather Research Forecast (WRF) climate model over 16 locations. The experiment results show that

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Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-Temporal Dynamicsimal padding strategy is directly linked to the data semantics. Furthermore, the inclusion of additional input spatial context or explicit physics-based rules allows a better handling of boundaries in particular for large number of recurrences, resulting in more robust and stable neural networks, wh

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A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression uncertainties. We show that while a convolutional network can be trained to correctly estimate well calibrated aleatoric uncertainty, -the uncertainty due to the presence of noise in the images- it is unable to generate a trustworthy ellipticity distribution when exposed to previously unseen data (