Parameter Reduction

Parameter reduction in machine learning focuses on shrinking model size without sacrificing accuracy, improving efficiency and enabling deployment on resource-constrained devices. Current research explores various techniques, including layer freezing, rank reduction, and tensor decomposition, applied to diverse architectures such as transformers, convolutional neural networks, and physics-informed neural networks. This work is significant because it addresses the growing need for computationally efficient models, impacting applications ranging from speech recognition and image generation to edge computing and real-time object detection. The resulting smaller, faster models are crucial for broader accessibility and deployment of advanced AI technologies.