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Comparative analysis of machine learning methods for active flow control [PDF]
Journal of Fluid Mechanics, 2022 Machine learning frameworks such as genetic programming and reinforcement learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, benchmarking some of their most representative algorithms against global
F. Pino +3 more
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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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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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Nature Communications, 2023 Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures.
Tao Wang +14 more
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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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Enabling robust offline active learning for machine learning potentials using simple physics-based priors [PDF]
Machine Learning: Science and Technology, 2020 Machine learning surrogate models for quantum mechanical simulations have enabled the field to efficiently and accurately study material and molecular systems.
Muhammed Shuaibi +3 more
semanticscholar +1 more source
Learning to Actively Learn Neural Machine Translation [PDF]
Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018 Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amount of the initial bitext. In this paper, we present a framework to learn sentence selection strategies for neural MT.
Wray Buntine +2 more
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A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions
IEEE Access, 2022 Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources.
Supriya V. Mahadevkar +6 more
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A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
Applied Sciences, 2023 In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generative ...
Feng Yi +3 more
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