Results 1 to 10 of about 154,891 (246)
Machine learning active-nematic hydrodynamics [PDF]
Proceedings of the National Academy of Sciences, 2021 Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of ...
Jonathan Colen +12 more
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Universal activation function for machine learning [PDF]
Scientific Reports, 2021 AbstractThis article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF’s parameters.
Brosnan Yuen +3 more
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Machine learning forecasting of active nematics [PDF]
Soft Matter, 2021 Our model is unrolled to map an input orientation sequence (from time t-8 to t-1) to an output one (t,t + 1…) with trajectray tracing. Cyan labels are −1/2 defect while purple ones are +1/2.
Zhengyang Zhou +8 more
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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
doaj +1 more source
Machine Learning for Active Portfolio Management [PDF]
The Journal of Financial Data Science, 2021 Machine learning (ML) methods are attracting considerable attention among academics in the field of finance. However, it is commonly believed that ML has not transformed the asset management industry to the same extent as other sectors. This survey focuses on the ML methods and empirical results available in the literature that matter most for active ...
Bartram, S. M. +3 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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source