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...
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...
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...
The accurate segmentation of seismic facies is of great significance to the study of sedimentary facies and the exploration and development of oil and gas
effectively guided the model to predict the masked nodes, thereby enabling it to capture the rich semantics of molecules. Moreover, this observation aligned with the finding in the field of CV that applying larger masking rates in self-supervised learning can yield improved performance on downstream...
The effective use of these tools and techniques within an organization can improve collaboration and working environment, enhance competitive advantage and responsiveness and increase overall productivity (Uriarte, 2008). A large number of KM tools have been developed that facilitate the management of ...
By utilizing prompt templates and leveraging medical knowledge, our approach intends to improve the adaptability of the model across departments and enable effective learning from limited labeled data. 2.3. Prompt learning The prompt learning method involves modifying the original text using templates to ...
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems). Survey Papers 2025 Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (WSDM, 2025) [paper][code] ...
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construct
using techniques such as sequence-to-sequence learning methods90,91. Third, the major bottleneck of the KIDS framework is its dependency on expert-guided manual curation of data in RDF triple format. An automated data curator would be a boon to adding information from existing literaure86,92. ...