(Include but not limited to)
Track 1: Deep Learning
Deep Representation Learning Generative model
Multimodal deep learning
Self-supervised learning
Large language models
Deep reinforcement learning
Small sample and zero sample learning
Interpretable deep learning
Edge and cloud-based deep learning
Trustworthy artificial intelligence
Applications of deep learning in human-computer interaction
Visual language models
Computer vision
Object detection learning
Emotion recognition
Adaptive deep systems
Applications of deep learning in robotics
Track 2: Neural Dynamics
Neural network dynamics (convergence, stability, bifurcation, chaos)
Pulse neural network (SNN) theory and learning algorithms
Neural computing hardware and systems
Neural field theory and brain network dynamics
Neuro-symbolic systems and cognitive computing
Applications of neural dynamics in intelligent control and optimization
Neurodynamic modeling of memory and learning
Biologically inspired neural networks (spatio-temporal dynamics, plasticity mechanisms)
Track 3: Complex Systems
Complex networks and dynamics
Multiscale modeling and computation
System identification and state estimation
Information-physical system modeling
Heterogeneous systems and discrete event systems
Data-driven system modeling
System reliability and resilience assessment
Complex system simulation and verification
Time series analysis and prediction
Other computer-related topics are also acceptable