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Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!
Engineering, 2023 By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted.
Yannick Ureel +6 more
doaj +4 more sources
Integrating Iterative Machine Teaching and Active Learning into the Machine Learning Loop
Procedia Computer Science, 2021 Scholars and practitioners are defining new types of interactions between humans and machine learning algorithms that we can group under the umbrella term of Human-in-the-Loop Machine Learning (HITL-ML).
Eduardo Mosqueira-Rey +1 more
exaly +4 more sources
Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction
Sensors, 2022 The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system.
Ibrahim M. El-Hasnony +3 more
doaj +4 more sources
Active learning machine learns to create new quantum experiments [PDF]
Proceedings of the National Academy of Sciences, 2017 Significance Quantum experiments push the envelope of our understanding of fundamental concepts in quantum physics. Modern experiments have exhaustively probed the basic notions of quantum theory.
A. Melnikov +6 more
semanticscholar +9 more sources
Machine Learning for Active Portfolio Management [PDF]
The Journal of Financial Data Science, 2021 Machine learning (ML) methods are attracting considerable attention among academics in the field of finance. However, it is commonly believed that ML has not transformed the asset management industry to the same extent as other sectors.
Söhnke M. Bartram +3 more
semanticscholar +3 more sources
Machine learning of molecular properties: Locality and active learning. [PDF]
The Journal of Chemical Physics, 2017 In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery.
Konstantin Gubaev +2 more
semanticscholar +5 more sources
PAL – parallel active learning for machine-learned potentials† [PDF]
Digital DiscoveryConstructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing data ...
Chen Zhou +7 more
doaj +7 more sources
Digital Chemical Engineering, 2022 In this work, we develop a model reduction method using sensitivity analysis and active learning to improve the computational efficiency of machine learning modeling of nonlinear processes.
Tianyi Zhao, Yingzhe Zheng, Zhe Wu
doaj +3 more sources
Visual Informatics, 2019 Supervised machine learning techniques require labelled multivariate training datasets. Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations. Using appropriate techniques,
Mohammad Chegini +5 more
doaj +2 more sources
Small data machine learning in materials science
npj Computational Materials, 2023 This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced.
Pengcheng Xu +3 more
doaj +2 more sources