Deep Multi-task Learning for Spatio-Temporal Incomplete Qualitative Event Forecasting

IF 8.9 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-09-13 DOI:10.1109/tkde.2024.3460539

Tanmoy Chowdhury, Yuyang Gao, Liang Zhao

引用次数: 0

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时空不完全定性事件预测的深度多任务学习

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Knowledge and Data Engineering 工程技术-工程：电子与电气

CiteScore

11.70

自引率

3.40%

发文量

515

审稿时长

6 months

期刊介绍： The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.