📚︎Track 1: Machine Learning Theory and Core Algorithms

• Deep Learning and Neural Network Architectures

• Reinforcement Learning and Decision Optimization

• Transfer Learning, Meta-Learning and Few-Shot Learning

• Self-Supervised and Contrastive Learning

• Federated Learning and Privacy-Preserving Machine Learning

• Graph Neural Networks and Graph Learning

• Explainable and Trustworthy Machine Learning

• Causal Inference and Causal Machine Learning

• Model Compression, Efficient Training and Inference

📚︎Track 2: Large Models and Generative AI

• Pre-training and Fine-tuning of Large Language Models

• Multimodal Foundation Models and Cross-Modal Learning

• Diffusion Models and Generative Adversarial Networks

• Retrieval-Augmented Generation and Knowledge Enhancement

• AI Agents and Tool Learning

• Prompt Engineering and In-Context Learning

• Alignment, Safety and Evaluation of Large Models

• Domain-Specific Large Models and Industry Applications

📚︎Track 3: Data Analysis, Mining and Data Intelligence

• Big Data Analytics and Distributed Computing

• Data Mining and Knowledge Discovery

• Time Series Analysis and Forecasting

• Anomaly Detection and Risk Identification

• Recommender Systems and User Behavior Analysis

• Data Governance, Data Quality and Data Cleaning

• Knowledge Graphs and Semantic Data Analysis

• Data Visualization and Visual Analytics

• Streaming Data and Real-Time Analytics

📚︎Track 4: Interdisciplinary Applications and Industry Practices

• Intelligent Healthcare and Bioinformatics

• FinTech and Intelligent Risk Control

• Intelligent Manufacturing and Industrial Big Data

• Smart Cities and Intelligent Transportation

• Computer Vision and Remote Sensing Image Analysis

• Natural Language Processing and Text Analysis

• Energy, Climate and Environmental Data Intelligence

• Educational Data Mining and Learning Analytics

• AI for Science