- About MLNN 2024 2024 International Conference on Machine Learning and Neural Networks As a branch of the 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL 2024), MLNN 2024 will focus on the fundamental theories, key technologies, and practical applications of learning systems and neural networks, encompassing various sub-fields such as deep learning, computer vision, natural language processing, and reinforcement learning. By means of invited presentations, keynote speeches, sub-conference presentations, poster sessions and other forms of communication channels available to us today; we aim to showcase the latest research findings and technological innovations in related fields from both academia and industry. This conference provides an opportunity for participants to gain deeper insights into cutting-edge trends in the field of learning systems and neural networks while broadening their research horizons through academic exchanges with peers. It also encourages cross-disciplinary exchange of state-of-the-art research information that connects advanced academic resources with industrial solutions by bringing together talent acquisition strategies alongside technology development initiatives. | - CALL FOR PAPERS - The topics of interest for submission include, but are not limited to: 1.Neural Network Deep Neural Network ... 2.Machine Learning Width Learning System ... For more information, please click: |
IMPORTANT DATES
Submission Deadline | Registration Deadline | Final Paper Submission Date | Conference Dates |
March 31,2024 | April 10,2024 | March 31,2024 | April 19-21, 2024 |
- SUBMISSION - Please send the full paper(word+pdf) to Submission System : Templates:Download For more information, please click: - PUBLICATION -
All papers submitted to MLNN 2024 will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted paperswill be accepted for the main conference, CVIDL 2024, for publication by IEEE (ISBN: 979-8-3503-7382-0) and submitted to IEEE Xplore, EI Compendex, Scopus for indexing. For more information, please click: |
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