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Machine Learning for Active Matter

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Active Learning From Imbalanced Data: A Solution of Online Weighted Extreme Learning Machine

IEEE Transactions on Neural Networks and Learning Systems, 2019
It is well known that active learning can simultaneously improve the quality of the classification model and decrease the complexity of training instances. However, several previous studies have indicated that the performance of active learning is easily
Shang Zheng, Changyin Sun, Hualong Yu
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Machine learning for active matter

Nature Machine Intelligence, 2020
The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random ...
F. Cichos, K. Gustavsson, B. Mehlig, G. Volpe   +3 more
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Improving high-impact bug report prediction with combination of interactive machine learning and active learning

Information and Software Technology, 2021
Xiang Chen, Xiaoxue Wu
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Sequential active learning using meta-cognitive extreme learning machine

Neurocomputing, 2016
Meng Joo Er
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AL-ELM: One uncertainty-based active learning algorithm using extreme learning machine

Neurocomputing, 2015
Changyin Sun, Hualong Yu, Xibei Yang
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Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification

, 2020
Hyperspectral imaging has been extensively utilized in several fields, and it benefits from detailed spectral information contained in each pixel, generating a thematic map for classification to assign a unique label to each sample.
Muhammad Ahmad, Sidra Shabbir, D. Oliva, M. Mazzara, Salvatore Distefano   +4 more
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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Molecular Informatics, 2021
Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit ...
Hampus Gummesson Svensson, Simon Johansson, E. Bjerrum, Alexander Schliep, M. Chehreghani, C. Tyrchan, O. Engkvist   +6 more
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Machine learning for activity pattern detection

Journal of Intelligent Transportation Systems, 2022
This paper proposes a data fusion approach to automatically detect activity patterns in a GPS dataset based on travel diaries and correct misclassification errors. The Activity Patterns Detection consists of a Supervised Learning framework, thanks to which the activity purposes in the travel diaries are learned and then predicted in the GPS dataset ...
Natalia Selini Hadjidimitriou, Guido Cantelmo, Constantinos Antoniou 0001   +2 more
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An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

, 2021
The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid ...
Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira   +2 more
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