Liu Ziyin (2024), ICML 2024 — ARXIV-2309.16932

Key takeaways

  • Mirror symmetries unify the common ones. Rescaling, rotation and permutation symmetries each imply reflection symmetries ℓ(θ) = ℓ((I − 2OOᵀ)θ).
  • Every mirror symmetry gives an absorbing constraint. Once Oᵀθ = 0, the gradient keeps it there for any minibatch and weight decay (Theorem 1, part 1; Corollary 1).
  • Weight decay and noise make it favoured. Above a critical weight decay, every minimum satisfies the constraint (part 4). With large learning rate or noisy gradients, the symmetric set is attractive for SGD even when it is a strict saddle for GD (§2.4, Eq. 5).
  • Structure as a corollary. Rescaling gives sparsity, rotation gives low rank, permutation gives homogeneous neurons. The structures vanish when the symmetry is removed (Fig. 3).

Standing in the record

Read as a follow-up to the law of balance (THEORY-211), for the discrete symmetries that law does not treat. The reading is NOTE-716, and the account filed from it is THEORY-213. It supplies the asymptotic half that THEORY-184 leaves open, and it offers a symmetry account of the collapse in THEORY-083.