Web: http://arxiv.org/abs/2206.08890

June 20, 2022, 1:11 a.m. | Ari Heljakka, Martin Trapp, Juho Kannala, Arno Solin

cs.LG updates on arXiv.org arxiv.org

It is prevalent and well-observed, but poorly understood, that two machine
learning models with similar performance during training can have very
different real-world performance characteristics. This implies elusive
differences in the internals of the models, manifesting as representational
multiplicity (RM). We introduce a conceptual and experimental setup for
analyzing RM and show that certain training methods systematically result in
greater RM than others, measured by activation similarity via singular vector
canonical correlation analysis (SVCCA). We further correlate it with predictive …

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