(Include but not limited to)
Track 1: Deep Learning Models and Algorithms
Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)
Deep Belief Network and Deep Boltzmann Machine
Autoencoder and Generative Adversarial Network (GAN)
Graph Neural Network and Graph Representation Learning
Transformer Architecture and Attention Mechanism
Deep Reinforcement Learning and Adaptive Decision Making
Meta-Learning and Deep Network
Deep Metric Learning Methods
Transfer Learning, Domain Adaptation and Multi-task Learning
Federated Learning and Edge Machine Learning
Self-supervised and Semi-supervised Learning
Online Learning and Incremental Learning
Small Sample and Zero Sample Learning
Neural Architecture Search (NAS)
Model Compression, Pruning and Quantization
Interpretable Deep Learning and Classification Explainability
Sparse Coding and Dimensionality Reduction Methods
Deep Kernel Learning and Gaussian Process
Diffusion Model and Energy-Based Generation Technology
Physical Information Neural Network (PINN)
Track 2: Large Model Architectures, Training and Systems
Architecture Design and Pre-training Methods for Large Language Models (LLMs)
Multimodal Large Models and Cross-modal Alignment
Hybrid Expert Model and Sparse Activation Architecture
Long Context Modeling and Attention Mechanism Optimization
Efficient Training of Large Models and Parallel Training Systems
Distributed Training, Communication Optimization and Load Balancing
Efficient Parameter Fine-tuning (LoRA, Adapter, Prompt Tuning, etc.)
Alignment of Large Models, RLHF and Instruction Fine-tuning
Accelerating Large Model Inference and Deployment Optimization
Security, Privacy and Anti-attack Defense of Large Models
Evaluation, Probe Analysis and Capability Boundaries of Large Models
Basic Models and Domain Adaptation
Interpretability of Large Models and Mechanism Explainability
Data Governance and Data Engineering of Large Model Training
Open-source Large Model Ecosystem and Tool Chain
Track 3: Generative AI, Multimodal and Cross-Applications
Text Generation, Dialogue Systems and Intelligent Writing
Image/Video Generation and Editing
Text to Image/Video/3D Generation
Multimodal Generation and Cross-modal Content Creation
Diffusion Model and Flow Matching Generation Methods
Controllability of Generative AI and Conditional Generation
Retrieval-Augmented Generation (RAG) Technology
Integration of Generative AI and Knowledge Graph
Agents (Agentic AI) and Autonomous Decision Making
Applications of Generative AI in Healthcare, Education, Finance, etc.
Applications of Generative AI in Scientific Research and Engineering
Ethics, Copyright and Content Authenticity of Generative AI
Detection of AI Generated Content and Deep Forging Identification
Human-Machine Collaborative Creation and Creative Support System
Embodied Intelligence and Generative World Model
......