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On the symmetries in the dynamics of wide two-layer neural networks

  • Received: 05 December 2022 Revised: 18 January 2023 Accepted: 01 February 2023 Published: 22 February 2023
  • We consider the idealized setting of gradient flow on the population risk for infinitely wide two-layer ReLU neural networks (without bias), and study the effect of symmetries on the learned parameters and predictors. We first describe a general class of symmetries which, when satisfied by the target function $ f^* $ and the input distribution, are preserved by the dynamics. We then study more specific cases. When $ f^* $ is odd, we show that the dynamics of the predictor reduces to that of a (non-linearly parameterized) linear predictor, and its exponential convergence can be guaranteed. When $ f^* $ has a low-dimensional structure, we prove that the gradient flow PDE reduces to a lower-dimensional PDE. Furthermore, we present informal and numerical arguments that suggest that the input neurons align with the lower-dimensional structure of the problem.

    Citation: Karl Hajjar, Lénaïc Chizat. On the symmetries in the dynamics of wide two-layer neural networks[J]. Electronic Research Archive, 2023, 31(4): 2175-2212. doi: 10.3934/era.2023112

    Related Papers:

  • We consider the idealized setting of gradient flow on the population risk for infinitely wide two-layer ReLU neural networks (without bias), and study the effect of symmetries on the learned parameters and predictors. We first describe a general class of symmetries which, when satisfied by the target function $ f^* $ and the input distribution, are preserved by the dynamics. We then study more specific cases. When $ f^* $ is odd, we show that the dynamics of the predictor reduces to that of a (non-linearly parameterized) linear predictor, and its exponential convergence can be guaranteed. When $ f^* $ has a low-dimensional structure, we prove that the gradient flow PDE reduces to a lower-dimensional PDE. Furthermore, we present informal and numerical arguments that suggest that the input neurons align with the lower-dimensional structure of the problem.



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