Papers › Towards Quantifying the Hessian Structure of Neural Networks
Towards Quantifying the Hessian Structure of Neural Networks
Zhaorui Dong, Yushun Zhang, Zhi-Quan Luo, Jianfeng Yao, Ruoyu Sun
Empirical studies reported that the Hessian matrix of neural networks (NNs) exhibits a near-block-diagonal structure, yet its theoretical foundation remains unclear. In this work, we reveal two forces that shape the Hessian structure: a ``static force'' rooted in the architecture design, and a ``dynamic force'' arisen from training. We then provide a rigorous theoretical analysis of ``static force'' at random initialization. We study linear models and 1-hidden-layer networks with the mean-square (MSE) loss and the Cross-Entropy (CE) loss for classification tasks. By leveraging random matrix theory, we compare the limit distributions of the diagonal and off-diagonal Hessian blocks and find that the block-diagonal structure arises as C →∞, where C denotes the number of classes. Our findings reveal that C is a primary driver of the near-block-diagonal structure. These results may shed new light on the Hessian structure of large language models (LLMs), which typically operate with a large C exceeding 10⁴ or 10⁵.
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