这本书名为《Knowledge-Guided Machine Learning: Accelerating Discovery Using Scientific Knowledge and Data》,由 Anuj Karpatne, Ramakrishnan Kannan, 和 Vipin Kumar 编辑。它涵盖了多个章节,每个章节都由该领域的领先研究人员撰写,探讨了科学知识引导的机器学习(KGML)的不同方面。以下是书中一些章节的主要内容概述...
Here, the authors propose a knowledge-guided machine learning framework that improves C cycle quantification in agroecosystems by integrating process-based and machine learning models, and multi-source high-resolution data.doi:10.1038/s41467-023-43860-5Liu, Licheng...
Knowledge GuidedMachineLearning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field Author: discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and resea...
题目:高通量表征力学论坛:Domain Knowledge-Guided Machine Learning 时间:2022年7月6日(周三)09:00 报告人:张统一 院士 主办方:浙江大学/天津大学/南方科技大学/西南交通大学 直播链接:蔻享--共享科学、…
Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management 来自 arXiv.org 喜欢 0 阅读量: 3 作者:SS Chatterjee,K Lindsay,N Chatterjee,R Patil,IA De Callafon,M Steinbach,D Giron,MH Nguyen,V Kumar 摘要: In recent years, the increasing threat of devastating wildfires ...
Researchers at Sun Yat-sen University Release New Data on Machine Learning (Knowledge-guided Multi-label Few-shot Learning for General Image Recognition) 来自 国家科技图书文献中心 喜欢 0 阅读量: 7 摘要: By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News...
et al. KG-Unet: a knowledge-guided deep learning approach for seismic facies segmentation. Earth Sci Inform 17, 1967–1981 (2024). https://doi.org/10.1007/s12145-024-01266-x Download citation Received15 December 2023 Accepted26 February 2024 Published08 March 2024 Issue DateJune 2024 DOIhttps...
geological knowledge-guided modelJohn Wiley & Sons, LtdJournal of Geophysical Research: Machine Learning and ComputationYing XuState Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences Wuhan ChinaRenguang ZuoState Key Laboratory of Geological Processes and Mineral ...
current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate...
[24] effectively classify diseases by combining rule-based characteristics with a knowledge-guided Convolutional Neural Network (CNN). Li et al. [25] leverage a multi-filter Residual CNN to predict ICD codes. Additionally, Chen et al. [26] introduce a bidirectional attention-based LSTM model ...