(The following topics include but are not limited to)
Track 1: Generative Artificial Intelligence
Large-scale language models and pre-training
Diffusion models and visual content generation
Multimodal generation (text-to-image, text-to-video, image-to-text, etc.)
Audio, music and speech generation
Code and structured data automatic generation
Controllable generation and conditional generation
Evaluation and benchmarking of generative models
Bias, fairness and security in generative AI
Retrieval-enhanced generation
Autonomous generation of agents and tools
Lightweighting of generative models and efficient inference
Ethics and copyright in generative AI
Track 2: Large Models
Generative large models
Visual pre-training model architectures
Large models integrating vision and optical sensing
Pre-trained language models
Multimodal fusion
Large model training and optimization
Large-scale distributed training
Model compression
Hardware acceleration and heterogeneous computing
Analysis of interpretability and robustness of visual large models, etc.
Track 3: Automated Generation
Automated machine learning (AutoML) and neural architecture search
Automated prompt engineering and optimization
Automated code generation and software engineering
Automatic synthesis of documents, reports and multimedia content
Data augmentation and synthetic data generation
Automated workflows and intelligent process orchestration
Automated generation for scientific discovery
Automated front-end and user interface generation
Programmatic generation of game content and virtual worlds
Automated testing and quality assurance
Autonomous agents and automated decision-making
Automated generation applications for industrial scenarios ......