arXiv:2605.18860v1 Announce Type: new Abstract: This paper proposes a neuron pruning framework based on neuron-level spectral structural importance evaluation. Given a trained neural network, we record the hidden states of each hidden layer during inference and model neurons as graph nodes, with hidden states treated as graph signals. Using ideas from graph signal processing, we infer layer-wise input and output graphs that characterize the structural relationships among neurons before and after each layer transformation. We then evaluate the spectral structural importance of neurons by analyz
Source: arXiv cs.LG — read the full report at the original publisher.