Track 3: Deep Learning and Explainable AI
Track Co-chair(s):
1. Tehseen Zia, COMSATS University Islamabad (CUI), Pakistan
The track targets cutting-edge research that advances the foundations of deep neural networks and the scientific understanding of why they work. We explicitly focus on architectures, training methodologies, reliability, fairness, interpretability, and deployment at scale.
Topics of interest include, but are not limited to:
- Novel Neural Architectures & Training Paradigms
- Optimization & Scalability Techniques
- Model Compression & Deployment
- Trustworthy & Robust Deep Learning
- Causal Representation Learning
- Federated & Privacy-Preserving Deep Learning
- Explainable AI Foundations
- Human-Centered XAI
- Ethics, Fairness & Responsible DL
- Multimodal & Graph Deep Learning (Methodology-Driven)
- Automated Machine Learning for Deep Nets
- Green AI & Sustainable DL