Complex Deep

Complex deep learning models are revolutionizing various fields, but their computational demands pose challenges for deployment on resource-constrained devices. Current research focuses on optimizing these models for efficiency, including exploring novel architectures (e.g., transformer-based networks, sparse networks), developing efficient training methods (e.g., knowledge distillation, quantization), and improving explainability through techniques like explanation ensembling. These efforts are crucial for enabling widespread adoption of AI in embedded systems and high-stakes applications while addressing concerns about energy consumption and model interpretability.