Results 11 to 20 of about 11,467,392 (329)
Active learning relevant vector machine for reliability analysis
Applied Mathematical Modelling, 2021 In this study, an adaptive relevant vector machine, which is developed within a probabilistic Bayesian learning framework, is combined with Monte Carlo simulation (MCS) to perform reliability analysis with high efficiency and accuracy.
T Z Li, Qiujing Pan, Daniel Dias
exaly +2 more sources
Calibration of uncertainty in the active learning of machine learning force fields
Machine Learning: Science and Technology, 2023 FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training.
Adam Thomas-Mitchell +2 more
doaj +2 more sources
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
openaire +4 more sources
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 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
Zhengyang Zhou +8 more
openaire +3 more sources
Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning [PDF]
Nature Communications, 2023 Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis.
Linus C. Erhard +3 more
semanticscholar +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
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
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
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.
Ming Liu 0028 +2 more
openaire +2 more sources