Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Multistage Multi-Task Learning for Quality Prediction of Multistage
  Manufacturing Systems   In multistage manufacturing systems, modeling multiple quality indices based
on the process sensing variables is important. However, the classic modeling
technique predicts each quality variable one at a time, which fails to consider
the correlation within or between stages. We propose a deep multistage
multi-task learning framework to jointly predict all output sensing variables
in a unified end-to-end learning framework according to the sequential system
architecture in the MMS. Our numerical studies and real case study have shown
that the new model has a superior performance compared to many benchmark
methods as well as great interpretability through developed variable selection
techniques.

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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci