CFP

The topics of interest for submission include, but are not limited to:

📚︎Deep Neural Network Architectures and Representation Learning

Deep neural network design and network regularization; Representation learning and automatic feature extraction; Neural network interpretability and model visualization methods; Recurrent neural networks and long short term memory networks; Graph neural networks and graph topology representation; Neural architecture search and automatic network topology design; Optimization strategies for deep neural network training; Network generalization performance and antioverfitting mechanisms; Multi scale feature learning and hierarchical representation; Lightweight neural network structure design

📚︎Generative Models and Advanced Neural Computing

Convolutional neural networks for visual tasks; Generative adversarial networks and generative model optimization; Variational autoencoders and probabilistic generative learning; Diffusion models and generative artificial intelligence; Image synthesis, image reconstruction and data augmentation; Multimodal generative model training and stability analysis; Sequence generation models for natural language processing; Neuralsymbolic hybrid reasoning and model integration; Knowledge graph embedding and neuraldriven knowledge reasoning; Hybrid intelligent systems combining symbolic logic and neural networks

📚︎Trustworthy Neural Networks and Intelligent Optimization

Explainable artificial intelligence for neural network systems; Lossfunctiondriven neural network optimization; Metaheuristic and swarmintelligencebased network optimization; Constrained optimization for neuralnetworkbased tasks; Surrogateassisted optimization for expensive neural model training; Multiobjective optimization for neural model hyperparameter tuning; Neural network robustness verification and adversarial defense; Knowledge distillation and model ensemble optimization; Optimization under noisy and uncertain data conditions; Neuralnetworkassisted combinatorial optimization

📚︎Interdisciplinary Engineering and Real World Applications

Healthcareoriented medical data analytics and neural network applications; Neuralnetworkdriven financial market analysis and trading strategy research; Energy system optimization, load forecasting and intelligent scheduling; Natural language processing and neuralbased text generation; Scientific computing and neuralnetworkaided engineering computation; Industrial timeseries prediction and anomaly detection; Neural network applications for imagebased industrial perception; Optimizationdriven resource allocation and system decisionmaking; Realworld deployment of neural networks on edge and embedded devices; Practical case studies of neural networks and intelligent optimization