Performing Model

Research on "performing models" spans diverse machine learning applications, focusing on improving model efficiency, accuracy, and reliability across various tasks. Current efforts concentrate on optimizing existing architectures like Transformers and convolutional neural networks, employing techniques such as model compression, knowledge distillation, and ensemble methods to enhance speed and performance while reducing computational costs. These advancements are crucial for deploying machine learning models in resource-constrained environments and improving the practical applicability of AI in fields ranging from industrial automation to medical image analysis and social media analytics.

Papers