(Topics include but are not limited to)
Design of Deep Neural Network Architecture
Generative model and diffusion model
Reinforcement Learning and Multi Agent Systems
Transfer learning, domain adaptation, and small sample learning
Graph Neural Networks and Knowledge Graph Reasoning
Frontiers of Self Supervision and Comparative Learning
Efficient training and fine-tuning of large-scale pre trained models
Model compression, pruning, and edge deployment
Interpretability and Fairness in Artificial Intelligence
Adversarial machine learning and robustness enhancement
Federated Learning and Privacy Protection Computing
Neural Symbol Learning and Hybrid Intelligence
Multimodal learning and cross modal generation
The Application of Machine Learning in Scientific Computing (AI for Science)
Time series data modeling and prediction
Innovation of Machine Learning in Computer Vision and Natural Language Processing
Brain like computing and neuromorphic learning
Machine Learning Automation and Meta Learning
Ethical, Security, and Governance Framework for Artificial Intelligence