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2026年第10届深度学习技术国际会议将于 2026年7月17-19日 在 中国昆明召开,由昆明理工大学主办,昆明理工大学信息工程与自动化学院承办。 为深度学习领域的研究人员,学者和科学家提供一个面对面交流自己的想法的机会 , 欢迎大家踊跃投稿参加!

我们邀请深度学习技术,及其相关领域的研究人员参与并提交论文稿件,ICDLT 主题范围包含:深入学习在软件中的应用, 深入学习在硬件中的应用, 关于深度学习的概念讲座和前沿研究, 建立以深度学习为核心的企业研究,等等...更多主题请查看 Call for Papers

You are invited to attend 2026 10th International Conference on Deep Learning Technologies will be held during July 17-19, 2026 in Kunming, China. It's sponsored by Kunming University of Science and Technology, China, organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology. It's to provide a valuable opportunity for researchers, scholars and scientists to exchange their ideas face to face in Deep Learning Technologies.

We invite researchers from deep learning technologies to participate and submit their work to the program. Likewise, any work on deep learning that has a relation to any of these fields or potential for the usage in any of them is welcome. Please refer to the different submission categories under "Call for Papers" above for further details.

Track 1: Deep Learning Model and Algorithm
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Track 2: Machine Learning Theory and Technology
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Track 3: Deep and Machine Learning Applications
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Track 4: Responsible AI, Security, and Governance
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Publication | Call-for-papers Flyer

ICDLT 2026 会议收录的文章将以论文集出版

The accepted paper will be included into ICDLT 2026 Conference Proceedings

Check the Publication History

Submission Deadline : March 1, 2026
Notification of Acceptance : April 1, 2026
Camera Ready Deadline: April 15, 2026
Registration Deadline: April 15, 2026
Conference Dates: July 17-19, 2026

■ • Submission System - 投稿系统 : https://www.zmeeting.org/submission/icdlt2026

 

Review Process:

By submitting a paper to ICDLT, the authors agree to the review process and understand that papers undergo a peer-review process. Manuscripts will be reviewed by appropriately qualified experts in the field selected by the Conference Committee, who will give detailed comments and — if the submission gets accepted — the authors submit a revised ("camera-ready") version that takes into account this feedback.

 

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