(The following topics include but are not limited to)
Track 1: Deep Learning
Basics of Deep Learning Algorithms
Deep Learning in Computer Vision
Combining Natural Language Processing with 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 2: Pattern Recognition
Artificial Intelligence Techniques in Pattern Recognition
Biometrics (including face recognition)
Computer Vision
Data Mining and Big Data
File Processing and Recognition
Advanced Learning Methods
Linear Models and Dimensionality Reduction
Machine Learning Methods
Model Representation and Selection
Fuzzy and Hybrid Techniques in PR
Image Processing and Analysis
Mathematical Theory of Pattern Recognition
Natural Language Processing and Recognition
Object Detection, Tracking and Recognition
Principles of Pattern Recognition
Robots, Remote Sensing
Shape and Texture Analysis
Statistical Pattern Recognition
Syntactic and Structural Pattern Recognition
Speech and Speech Recognition
Track 3: Neural Networks
Deep Learning Algorithms and Applications
Applications of Convolutional Neural Networks (CNN) in Image Processing
Recurrent Neural Networks (RNN) and Their Applications in Sequential Data
Autoencoders and Generative Models
Applications of Neural Networks in Natural Language Processing (NLP)
Applications of Neural Networks in Medical Image Analysis
Applications of Neural Networks in Bioinformatics
Applications of Neural Networks in Intelligent Transportation Systems
Applications of Neural Networks in Robot Control
Applications of Neural Networks in Anomaly Detection and Early Warning
Federated Learning and Distributed Neural Networks
Hardware Acceleration of Neural Networks
Applications of Neural Networks in the Internet of Things (IoT)
Applications of Neural Networks in Edge Computing
Neural Networks and Photonics ...