Stochastic weight matrix dynamics during learning and Dyson Brownian motion
<|reference_start|>Stochastic weight matrix dynamics during learning and Dyson Brownian motion: We demonstrate that the update of weight matrices in learning algorithms can be described in the framework of Dyson Brownian motion, thereby inheriting many features of random matrix theory. We relate the level of stochasticity to the ratio of the learning rate and the mini-batch size, providing more robust evidence to a previously conjectured scaling relationship. We discuss universal and non-universal features in the resulting Coulomb gas distribution and identify the Wigner surmise and Wigner semicircle explicitly in a teacher-student model and in the (near-)solvable case of the Gaussian restricted Boltzmann machine.<|reference_end|>
@article{aarts2024stochastic,
    title={Stochastic weight matrix dynamics during learning and Dyson Brownian
  motion},
    author={Gert Aarts, Biagio Lucini and Chanju Park},
    journal={arXiv preprint arXiv:2407.16427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16427},
    primaryClass={cond-mat.dis-nn cs.LG hep-lat}
}