Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks
<|reference_start|>Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks: We study a continuous-time approximation of the stochastic gradient descent process for minimizing the expected loss in learning problems. The main results establish general sufficient conditions for the convergence, extending the results of Chatterjee (2022) established for (nonstochastic) gradient descent. We show how the main result can be applied to the case of overparametrized linear neural network training.<|reference_end|>
@article{lugosi2024convergence,
    title={Convergence of continuous-time stochastic gradient descent with
  applications to linear deep neural networks},
    author={Gabor Lugosi and Eulalia Nualart},
    journal={arXiv preprint arXiv:2409.07401},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07401},
    primaryClass={cs.LG math.OC stat.ML}
}