Knowledge discovery in Chinese herbal medicine: a machine learning perspective [PDF]
MATEC Web of Conferences, 2021 Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients.
Liang Nan, Liang Qing, Ji Fenglei
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Machine learning for phase behavior in active matter systems [PDF]
Soft Matter, 2021 We demonstrate that deep learning techniques can be used to predict motility-induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level.
Austin R. Dulaney, John F. Brady
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Active learning for bird sound classification via a kernel-based extreme learning machine.
Journal of the Acoustical Society of America, 2017 In recent years, research fields, including ecology, bioacoustics, signal processing, and machine learning, have made bird sound recognition a part of their focus. This has led to significant advancements within the field of ornithology, such as improved
Kun Qian +3 more
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Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. [PDF]
Journal of Chemical Physics, 2023 A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows ...
J. Carrete +4 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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Active-Learning Approaches for Landslide Mapping Using Support Vector Machines
Remote Sensing, 2021 Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models.
Zhihao Wang, Alexander Brenning
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Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning [PDF]
Physical review B, 2018 We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly.
E. Podryabinkin +3 more
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Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning [PDF]
E3S Web of Conferences, 2021 Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system.
An Chang
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MLIP-3: Active learning on atomic environments with moment tensor potentials. [PDF]
Journal of Chemical Physics, 2023 Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena but also on sharing computer codes developed within the community.
E. Podryabinkin +3 more
semanticscholar +1 more source
HARTH: A Human Activity Recognition Dataset for Machine Learning [PDF]
Sensors, 2021 Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work.
Aleksej Logacjov +4 more
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