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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
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Active learning machine learns to create new quantum experiments [PDF]
Proceedings of the National Academy of Sciences, 2018 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. Arguably, further breakthroughs require the tackling of complex quantum phenomena and consequently require complex experiments and involved ...
Alexey A. Melnikov +6 more
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How Much a Model be Trained by Passive Learning Before Active Learning?
IEEE Access, 2022 Most pool-based active learning studies have focused on query strategy for active learning. In this paper, via empirical analysis on the effect of passive learning before starting active learning, we reveal that the amount of data acquired by passive ...
Dae Ung Jo, Sangdoo Yun, Jin Young Choi
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Improving molecular machine learning through adaptive subsampling with active learning
Digital Discovery, 2023 Active machine learning can be used to sample training data in an autonomous manner to improve machine learning performance. This approach is competitive with state-of-the-art data sampling approaches, especially on erroneous data.
Yujing Wen +3 more
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Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges
Applied Sciences, 2022 Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as ...
Mingfei Wu, Chen Li, Zehuan Yao
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QuantuMoonLight: A low-code platform to experiment with quantum machine learning
SoftwareX, 2023 Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms.
Francesco Amato +18 more
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Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters [PDF]
, 2019 We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper.
Chen, Yang +9 more
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Discriminative Cooperative Networks for Detecting Phase Transitions [PDF]
, 2018 The classification of states of matter and their corresponding phase transitions is a special kind of machine-learning task, where physical data allow for the analysis of new algorithms, which have not been considered in the general computer-science ...
Liu, Ye-Hua +1 more
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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
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Classifying Human Activities Using Machine Learning and Deep Learning Techniques
, 2023 Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a healthy way.
Uday, Sanku Satya +3 more
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