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

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PuzzleShuffle: Undesirable Feature Learning for Semantic Shift Detectionon operations. Deep neural networks have attained remarkable performance in various tasks when the data distribution is consistent between training and operation phases, but performance significantly drops when they are not. The challenge of detecting Out-of-Distribution (OoD) data from a model that

袋鼠

Chromatic

AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challengeutomated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020. We present its design, analysis, and post-hoc experiments. The code submission requirement precluded participants from any manual intervention, testing automated machine learning capabilities of solutions, across many data

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Methods for Automatic Machine-Learning Workflow Analysisteps and consider different stages like development, testing or deployment. Managing workflows poses several challenges, such as workflow versioning, sharing pipeline elements or optimizing individual workflow elements - tasks which are usually conducted manually by data scientists. A dataset contai

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ConCAD: Contrastive Learning-Based Cross Attention for Sleep Apnea Detection approach. However, the hand-crafted expert knowledge-based features are still insightful. These expert-curated features can increase the model’s generalization and remind the model of some data characteristics, such as the time interval between two patterns. It is particularly advantageous in tasks

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DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Networkempirical parameterization schemes. For example, planetary boundary layer (PBL) parameterizations are used in atmospheric models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer—the lowest part of the atmosphere. We consider the problem of developing

MOT

Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-Temporal Dynamicsated problems calls for an improved understanding of boundary condition treatment, and its influence on the network accuracy. In this paper, several strategies to impose boundary conditions (namely padding, improved spatial context, and explicit encoding of physical boundaries) are investigated in t

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A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression weak gravitational lensing along the line of sight. It can be used as a tracer of the matter distribution in the Universe. The unbiased estimation of the local value of the cosmic shear can be obtained via Bayesian analysis which relies on robust estimation of the galaxies ellipticity (shape) poste