English(EN) A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks

新理论为神经网络提供闭式深度-宽度律

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员为深度线性网络中的神经坍塌推导出了闭式深度-宽度律，这提供了一种在训练前理解网络架构如何影响学习到的表示的方法。该定律以神经坍塌为探针，用学习到的特征核来表示特定的神经坍塌指标。研究结果表明，在宽度较大时，累积的压缩是深度与宽度比的函数，其速率由数据、先验和架构决定。 AI

影响为理解神经网络架构和表示学习提供了理论框架。

排序理由详细介绍机器学习理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Pankhil Gawade, Kirsten Fischer, Guy Wolf, Ewa Szczurek · 2026-10-16 04:00

深度线性网络中神经坍塌的闭式深度宽度律

arXiv:2610.21820v1 Announce Type: new Abstract: How depth and width of neural networks shape learned representations is usually studied only after training. We show that, in a tractable feature-learning setting, this dependence can instead be obtained in closed form. Using neural…