Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of StochasticityDownload PDF

21 May 2021, 20:46 (edited 28 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Implicit bias, SGD, diagonal neural networks, SDE, implicit regularisation
  • TL;DR: Our paper deals with the implicit bias of SGD for diagonal linear networks.
  • Abstract: Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through its continuous time version, namely stochastic gradient flow. We explicitly characterise the solution chosen by the stochastic flow and prove that it always enjoys better generalisation properties than that of gradient flow.Quite surprisingly, we show that the convergence speed of the training loss controls the magnitude of the biasing effect: the slower the convergence, the better the bias. To fully complete our analysis, we provide convergence guarantees for the dynamics. We also give experimental results which support our theoretical claims. Our findings highlight the fact that structured noise can induce better generalisation and they help explain the greater performances of stochastic gradient descent over gradient descent observed in practice.
  • Supplementary Material: zip
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code: zip
12 Replies