(Topics include but are not limited to)
New Deep Learning Architecture and Algorithms
Generative models (diffusion, GAN, flow models)
Reinforcement Learning and Multi Agent Systems
Self supervision and contrastive learning
Transfer Learning and Domain Adaptation
Graph Neural Networks and Knowledge Graph
Large scale pre training models and fine-tuning
Model compression, pruning, and edge deployment
Interpretability and Fairness AI
Federated Learning and Privacy Protection
Multimodal learning and cross modal generation
Time series analysis and prediction
Data Mining and Feature Engineering
Big data analysis and distributed computing
Anomaly detection and robustness learning
The Application of AI in Scientific Computing
Computer Vision and Image Processing
Natural Language Processing and Understanding
Recommendation System and User Modeling
Automated Machine Learning (AutoML)
Meta learning and small sample learning
AI Ethics, Security, and Governance
Quantum Machine Learning
Edge Intelligence and Embedded AI
Data driven decision-making and optimization