Onception: Active Learning with Expert Advice for Real World Machine Translation
Computational Linguistics, 2023 Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate.
Vânia Mendonça +3 more
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Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning [PDF]
npj Computational MaterialsUnderstanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the
Abhishek Sharma, Stefano Sanvito
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ACTIVE LEARNING ON LARGE HYPERSPECTRAL DATASETS: A PREPROCESSING METHOD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Machine learning algorithms demonstrated promising results for hyperspectral semantic segmentation. However, they strongly rely on the quality of training datasets.
R. Thoreau +5 more
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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
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A Classification Method of Agricultural News Text Based on BERT and Deep Active Learning [PDF]
Nongye tushu qingbao xuebao, 2022 [Purpose/Significance] At present, most of the training models used in the research of news classification are non-active learning. There are common problems about these models, including data cannot be labeled immediately and the labeling cost is too ...
SHI Yunlai, CUI Yunpeng, DU Zhigang
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AI-Assisted Cotton Grading: Active and Semi-Supervised Learning to Reduce the Image-Labelling Burden
Sensors, 2023 The assessment of food and industrial crops during harvesting is important to determine the quality and downstream processing requirements, which in turn affect their market value. While machine learning models have been developed for this purpose, their
Oliver J. Fisher +4 more
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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
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ICS: Total Freedom in Manual Text Classification Supported by Unobtrusive Machine Learning
IEEE Access, 2022 We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification. The application uses machine learning to continuously fit automatic classification models that are in turn used to ...
Andrea Esuli
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MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research
npj Computational Materials, 2022 Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research.
Keith A. Brown
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Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning
Entropy, 2022 Active learning is a technique for maximizing performance of machine learning with minimal labeling effort and letting the machine automatically and adaptively select the most informative data for labeling.
Hendra Kurniawan, Masahiro Mambo
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