(Including but not limited to the following topics)
Track 1: Neural Networks
New neural network architectures (Transformer, graph neural networks, capsule networks, etc.)
Mathematical foundation and theoretical analysis of neural networks
Neural architecture search (NAS) and automated machine learning
Interpretable neural networks and cognitive models
Spiking neural networks and neuromorphic computing
Biologically inspired neural networks and brain connectomics modeling
Lightweight networks and edge intelligence deployment
Dynamic neural network training and optimization theory
Track 2: Deep Learning
Basic algorithms of deep learning
Deep learning in computer vision
Combination of natural language processing and vision
Transfer learning
Domain adaptation
Reinforcement learning
Joint learning and privacy protection
Self-supervised and unsupervised learning
Interpretability of deep learning models
Edge computing
Track 3: Intelligent Computing
Cloud computing and edge computing
Distributed and high-performance computing
Quantum computing and quantum machine learning
Neuromorphic computing and brain-inspired intelligence
Computational storage and near-data processing
Photon computing and new computing paradigms
Intelligent chips and hardware acceleration
New storage architectures and systems
High-performance networks and communications
Green computing and energy efficiency optimization
Digital twin and virtual simulation
Metaverse and immersive computing
Blockchain and trusted computing
Internet of Things and intelligent sensing
Embedded and mobile computing
Matrix computation and tensor analysis
Differential equations and dynamical systems
Big data analysis and mining
Data visualization and interaction
Time series analysis and predictive modeling ......