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Showing 1 to 3 of 3 for “"science-guided machine learning"”.

  1. Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts

    … amount of labeled data available has helped tend machine learning (ML) research toward using purely data driven end-to-end pipelines, e.g., in deep neural network research. However, in many situations, data is limited and of poor quality. Traditional ML pipelines are known to be susceptible to …

    vt Repository record for Science Guided Machine Learning: Incorporating Scientific Domain Knowledge for Learning Under Data Paucity and Noisy Contexts (opens in a new tab)

  2. Integrated Process Modeling and Data Analytics for Optimizing Polyolefin Manufacturing

    … in a complex system. Data analytics and machine learning (ML) have been applied in the chemical process industry for accurate predictions for data-based soft sensors and process monitoring/control. Specifically, for polymer processes, they are very useful since the polymer quality …

    vt Repository record for Integrated Process Modeling and Data Analytics for Optimizing Polyolefin Manufacturing (opens in a new tab)

  3. Towards interactive analytics over voluminous spatiotemporal data using a distributed, in-memory framework

    … the potential of leveraging various transfer learning techniques to improve the turn-around times of our memory-resident deep learning models, given the voluminous nature of our datasets, while maintaining good overall accuracy over its entire spatiotemporal domain. Additionally, our research …

    colostate Repository record for Towards interactive analytics over voluminous spatiotemporal data using a distributed, in-memory framework (opens in a new tab)