Understanding Weight Similarity of Neural Networks via Chain Normalization Rule and Hypothesis-Training-Testing. (arXiv:2208.04369v1 [cs.LG])

We present a weight similarity measure method that can quantify the weight
similarity of non-convex neural networks. To understand the weight similarity
of different trained models, we propose to extract the feature representation
from the weights of neural networks. We first normalize the weights of neural
networks by introducing a chain normalization rule, which is used for weight
representation learning and weight similarity measure. We extend the
traditional hypothesis-testing method to a hypothesis-training-testing
statistical inference method to validate the hypothesis …

arxiv hypothesis lg networks neural networks normalization testing training understanding