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MLMI 2027

Full name: 2027 The 10th International Conference on Machine Learning and Machine Intelligence (MLMI 2027)
Abbreviation: MLMI 2027
Tokyo, Japan 
July 16-19, 2027

Website: https://www.mlmi.net/



Publication:
Submitted papers will be peer reviewed by conference committees, and accepted papers after proper registration and presentation will be published in the Conference Proceedings of MLMI 2027. 
(All the previous Conference Proceedings have been archived in ACM Digital Library and have been indexed by Ei Compendex & Scopus.)

Topics
Topics of interest for submission include, but are not limited to:
Track 1: Deep Learning and Neural Architectures
Architectural Advances
Efficient Training Techniques
Graph Neural Networks
Generative Models
Neural Architecture Search
Transfer Learning

Track 2: Reinforcement Learning and Sequential Decision Making
Advanced Reinforcement Learning Algorithms
Multi-Agent Reinforcement Learning
Time Series and Sequential Data
Predictive Modeling
Anomaly Detection
Sequential Decision Making
Real-Time Al Systems

Track 3: Applied Machine Intelligence
Machine Learning in Healthcare
Al in Robotics
Al for Cybersecurity
Biometric and Behavioral Analytics
Industry-Specific Applications

Track 4: Natural Language Processing and Multimodal Learning
Large Language Models
Multilingual and Low-Resource NLP
Multimodal Machine Learning
Cross-Modal Retrieval and Generation

Track 5: Emerging Paradigms and Future Directions
Quantum Machine Learning
Federated and Distributed Learning
AutoML and Meta-Learning
Sustainable and Green Al
Hybrid Models

Track 6: Explainable, Ethical, and Human-Centered Al
Explainable Al (XAI)
Ethical Al and Fairness
Privacy-Preserving Machine Learning
Human-Centered Al
Augmented Intelligence

Submission Guideline
English is the official language. Paper should be prepared in English.
Abstract submission is for presentation only without publication.
Full paper submission is for both presentation and publication. (No less than 10 pages)


Contact

Conference Secretary: Miss Tessa Chen
Email: mlmi_contact@163.com
Tel: +86-13103333373
Website: https://www.mlmi.net/


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