• Corpus ID: 248227825

Finding Materialized Models for Model Reuse

  title={Finding Materialized Models for Model Reuse},
  author={Minjun Zhao and Lu Chen and Keyu Yang and Yuntao Du and Yunjun Gao},
—Materialized model query aims to find the most appropriate materialized model as the initial model for model reuse. It is the precondition of model reuse, and has recently attracted much attention. Nonetheless, the existing methods suffer from low privacy protection, limited range of applications, and inefficiency since they do not construct a suitable metric to measure the target-related knowledge of materialized models. To address this, we present MMQ, a privacy-protected, general, efficient… 


Don't Fear the REAPER: A Framework for Materializing and Reusing Deep-Learning Models
  • Melanie B. Sigl
  • Computer Science
    2019 IEEE 35th International Conference on Data Engineering (ICDE)
  • 2019
The aim of this research is to reduce training time of machine learning from a data-management perspective through model reuse, and shed some light on the above relationship in the case when reusing a model is appropriate.
On automated source selection for transfer learning in convolutional neural networks
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Instead of prescribing a structured data model for data science projects, this work takes an information retrieval approach by decomposing the discovery task into three major steps: project query and matching, model comparison and ranking, and processing and building ensembles with returned models.
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Source-selectionfree transfer learning
  • IJCAI, pages 2355–2360
  • 2011