Title: On the Power-Law Hessian Spectra in Deep Learning. Abstract: It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning. Recent works empirically discovered that the Hessian spectrum in deep learning has a two-component structure that consists of a small number of large eigenvalues and a large number of nearly-zero eigenvalues. However, the mathematical structure behind the Hessian spectra is still under-explored. To the best of our knowledge, we are the first to demonstrate that the Hessian spectra of well-trained deep neural networks exhibit simple power-law structures. Inspired by the statistical physics theories, we provide a maximum-entropy theoretical interpretation for explaining why the power-law structure exists. Our extensive experiments using the novel power-law spectral method reveal that the power-law Hessian spectra critically relate to multiple important behaviors of deep learning, including optimization, generalization, overparameterization, and overfitting.