Alvaro Ribot, Yulia Alexandr, Anna Seigal · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28439 · open-access PDF
神经模型是可以通过特定的神经网络架构表达的函数的集合。神经模型包含自然子模型,例如通过设置一些神经元不活跃而获得的模型。我们研究神经模型的几何形态,并询问他们是否喜欢他们的子模型。由此,我们指的是损失功能的本地最小化剂是否存在于特定的子模型中。我们引入一个地理框架来确定这种情况是否发生。对于浅层多层感知器,我们表明具有不活跃隐藏神经元的子网络从来不优先。对于深层多层感知器,我们提供条件
Machine translation (Meta M2M100); the English original follows.
A neural model is the set of functions that can be expressed by a given neural network architecture. Neural models contain natural submodels, such as those obtained by setting some neurons to be inactive. We study the geometry of neural models and ask whether they prefer their submodels. By this we mean whether local minimizers of the loss function lie in a given submodel. We introduce a geometric framework to determine if this occurs. For shallow multilayer perceptrons, we show that subnetworks with inactive hidden neurons are never preferred. For deep multilayer perceptrons, we give conditio
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