IEEE BigData 2021 Cup: Soft Sensing at Scale
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Abstract (arXiv)
IEEE BigData 2021 Cup: Soft Sensing at Scale is a data mining competition organized by Seagate Technology, in association with the IEEE BigData 2021 conference. The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques. In this paper we go into the details of the challenge and describe the data set provided to participants. We define the metrics of interest, baseline models, and describe approaches we found meaningful which may be a good starting point for further analysis. We discuss the results obtained with our approaches and give insights on what potential challenges participants may run into. Students, researchers, and anyone interested in working on a major industrial problem are welcome to participate in the challenge!
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 7 Sep 2021.
Builds on
5 of 14 references.
References · 5
- 2017Attention Is All You Needcited by 59,932
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
- 2018Multivariate LSTM-FCNs for Time Series Classificationcited by 120
- 2021Auto-encoder based Model for High-dimensional Imbalanced Industrial Datacited by 6
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Citing papers per year: 2021: 1.
1 paper.
- 2021GraSSNet: Graph Soft Sensing Neural Networkscited by 5
- 2021GraSSNet: Graph Soft Sensing Neural Networks12 Nov 2021