Weight Similarity

Weight similarity research focuses on quantifying and understanding the relationships between the parameters (weights) of different neural network models, particularly exploring how similar weights lead to similar model performance. Current research employs techniques like chain normalization and hierarchical clustering algorithms to analyze weight similarity across various architectures, including Multi-Layer Perceptrons, Convolutional Neural Networks, and Recurrent Neural Networks. These investigations aim to improve our understanding of neural network optimization and generalization, potentially leading to more efficient training methods and better model interpretability.