The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur

Research output: Contribution to conferencePosterpeer-review


In this paper, we conjecture that if the permutation invariance of neural networks is taken intoaccount, SGD solutions will likely have no barrier in the linear interpolation between them. Althoughit is a bold conjecture, we show how extensive empirical attempts fall short of refuting it. We furtherprovide a preliminary theoretical result to support our conjecture. Our conjecture has implications forlottery ticket hypothesis, distributed training and ensemble methods
Original languageEnglish
Publication statusPublished - 7 Jul 2021
EventSparsity in Neural Networks - Advancing Understanding and Practice: SNN Workshop 2021 - Virtual
Duration: 8 Jul 20219 Jul 2021


WorkshopSparsity in Neural Networks - Advancing Understanding and Practice
Internet address


  • deep learnig
  • loss landscape
  • optimization

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