Weight Distribution

Weight distribution, encompassing both the spatial distribution of mass (e.g., in gait analysis) and the distribution of parameters within neural networks, is a crucial area of research with implications for diverse fields. Current investigations focus on optimizing weight distributions for improved neural network performance, including exploring the impact of weight normalization, quantization, and pruning techniques on accuracy, robustness, and energy efficiency, often employing deep learning models and algorithms like AdamW and various pruning methods. These studies aim to enhance the understanding of neural network learning dynamics and improve the design of efficient and reliable artificial intelligence systems, as well as providing more accurate and objective diagnostic tools in biomechanics.