Learnable Parameter

Learnable parameters are adjustable components within machine learning models that are optimized during training to improve performance on a given task. Current research focuses on developing parameter-efficient methods, such as low-rank adaptations and techniques that selectively learn only subsets of parameters, to reduce computational costs and memory demands, particularly for large language and vision models. This area is crucial for advancing model efficiency, interpretability, and scalability, enabling deployment of powerful models on resource-constrained devices and facilitating the development of more robust and adaptable AI systems.