Call For Papers

Feedforward neural networks

Recurrent neural networks

Convolutional neural networks

Dynamic neural networks

State-space neural models

Radial basis function networks

Self-organizing maps

Associative memory and attractor networks

Neural architecture design

Expressivity and generalization of neural networks

Theory of neural networks and deep learning

Optimization techniques for neural networks

Self-supervised learning

Contrastive learning

Unsupervised learning

Information-theoretic learning

Feature selection and extraction

Generative adversarial networks

Variational autoencoders

Diffusion and flow-based models

Foundation models based on neural networks

Transformer and attention-based neural architecture

Pre-training and fine-tuning

Parameter-efficient adaptation and alignment

Prompt engineering

Context engineering

Graph neural networks

Geometric deep learning

Learning on relational and structured data

Temporal graph neural networks

Knowledge graph neural learning

Reinforcement learning

Deep reinforcement learning

Adaptive critic designs

Approximate dynamic programming

Multi-agent reinforcement learning and game-theoretic learning

Reinforcement learning for control and decision systems

Agentic RL

Continual learning

Lifelong learning

Catastrophic forgetting mitigation

Online and incremental learning

Meta-learning, few-shot and zero-shot learning

Domain adaptation and curriculum learnin

Efficient and tiny neural networks

Resource-constrained neural networks

Model compression and pruning

Quantization and low-precision neural learning

Edge and embedded AI

Sustainable and green AI

Cellular Computational Networks

Federated learning

Distributed learning

Decentralized learning

Communication-efficient and resilient learning

Scalable neural network training

Interpretable and explainable AI

Trustworthy and reliable AI

Robust neural networks

Privacy-preserving learning

Safety AI

Ethics and regulations in AI

Multimodal learning

Vision-language models

Perceptual neural networks

Cross-modal representation learning

Multimodal reasoning and generation

Neuro-symbolic AI

Knowledge representation and reasoning

Neural reasoning systems

Neuro-fuzzy systems

Knowledge-guided AI

Neuromorphic systems

Spiking neural networks

Brain-inspired neural computation

Event-driven neural processing

Reservoir and echo-state networks

Computational neuroscience

Nervous system modeling

Brain network analysis

Brain-machine interfaces

Cognitive models

Neurophysiological data analysis

Neural engineering

Bayesian neural networks

Probabilistic neural learning

Variational inference with neural networks

Uncertainty estimation and calibration

Causal ML

Gaussian processes and energy-based models

Collective intelligence

Ensemble neural learning

Modular neural networks

Mixture-of-experts architectures

Collective or Swarm intelligence

Physics-informed neural networks

Neural operators and PDF solvers

Scientific ML

Complex system modeling

AI for critical infrastructures

AI for healthcare and life sciences

AI for education

AI for climate sciences

AI for space

Image and video processing

Speech and audio processing

Time-series analysis

Recommender systems

Robotics

Manufacturing

Transportation

Computer graphics

Communications

Bioinformatics and biomedical engineering

Finance

Agentic AI systems

Embodied intelligence

Neural networks for robotics

Autonomous decision-making systems

Human–agent collaboration

Quantum neural networks

Quantum AI

Quantum ML

Hybrid quantum-classical neural models

Quantum learning systems