Personalized Attention

Personalized attention in machine learning focuses on tailoring models to individual users or data sources, addressing the limitations of generic models in handling heterogeneous data. Current research emphasizes the use of attention mechanisms within deep learning architectures, particularly transformers and federated learning frameworks, to achieve this personalization, often incorporating techniques like personalized self-attention layers or client-specific attention blocks. This approach improves model performance and generalizability across diverse datasets, with applications ranging from human-robot interaction and medical image analysis to personalized recommendations and dialogue generation.