March 14, 2024, 4:41 a.m. | Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad, Seong Joon Oh

cs.LG updates on

arXiv:2403.07968v1 Announce Type: new
Abstract: Entezari et al. (2022) conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances. This means that two independent solutions can be connected by a linear path with low loss, given one of them is appropriately permuted. However, current methods to test this theory often fail to eliminate loss barriers between two independent solutions (Ainsworth et al., 2022; Benzing et al., 2022). In this work, we conjecture that …

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