首页|Researchers from Beijing University of Technology Provide Details of New Studies and Findings in the Area of Machine Learning (The Pre-trained Explainable Deep Learning Model With Stacked Denoising Autoencoders for Slope Stability Analysis)
Researchers from Beijing University of Technology Provide Details of New Studies and Findings in the Area of Machine Learning (The Pre-trained Explainable Deep Learning Model With Stacked Denoising Autoencoders for Slope Stability Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "In this work, we proposed a deeply- integrated explainable pre-trained deep learning framework with stacked denoisin g autoencoders in the assessment of slope stability. The deep learning model con sists of a deep neural network as a trunk net for prediction and autoencoders as branch nets for denoising." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Univers ity of Technology, "A comprehensive review of machine learning algorithms in slo pe stability evaluation is first given in the introduction section. A series of 530 data is then collected from real slope records, which are visualized and inv estigated in feature engineering and further preprocessed for model training. To ensure reliable and trustworthy model interpretability, a unified model from bo th local and global perspectives is integrated into the deep learning model, whi ch incorporated the ad hoc back-propagation based Deep SHAP, perturbation based Kernel SHAP and PDPs, and distillation based LIME and Anchors. For a fair evalua tion, repeated stratified 10-fold cross-validation is adopted in model evaluatio n. The obtained results manifest that the constructed model outperforms commonly used machine learning methods in terms of accuracy and stability on the real-wo rld slope data."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningBeijing University of Techn ology
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