Linear mode-connectivity (LMC) (or lack thereof) is one of the intriguing
characteristics of neural network loss landscapes. While empirically well
established, it unfortunately still lacks a proper theoretical understanding.
Even worse, although empirical data points are abound, a systematic study of
when networks exhibit LMC is largely missing in the literature. In this work we
aim to close this gap. We explore how LMC is affected by three factors: (1)
architecture (sparsity, weight-sharing), (2) training strategy (optimization
setup) as well as (3) the underlying dataset. We place particular emphasis on
minimal but non-trivial settings, removing as much unnecessary complexity as
possible. We believe that our insights can guide future theoretical works on
uncovering the inner workings of LMC.

神经网络损失地形的线性模式连接 (或缺乏连接) 是一个有趣的特性，尽管在经验上已经被证实，但还缺乏适当的理论理解。本文通过探讨三个因素（架构、训练策略和底层数据集）对线性模式连接的影响，旨在弥补文献中对当网络展示线性模式连接的系统性研究的不足。我们特别关注最少但非平凡的设置，以尽可能消除不必要的复杂性，并相信我们的观察能够指导未来揭示线性模式连接内部机制的理论研究。