Limited Resource

Limited resource scenarios in machine learning and related fields are driving research focused on optimizing model efficiency and training processes. Current efforts concentrate on developing lightweight architectures, employing advanced optimization techniques (like compositional optimization and novel optimizers), and implementing efficient data augmentation strategies to achieve high performance with minimal computational resources and data. This research is crucial for broadening access to advanced AI technologies, enabling deployment on resource-constrained devices (e.g., mobile phones, embedded systems), and promoting fairness in applications where resource allocation is a critical factor.