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cs.LG2025
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations
Enric Boix-Adsera, Neil Mallinar, James B. Simon +1
It is a central challenge in deep learning to understand how neural networks learn representations. A leading approach is the Neural Feature Ansatz (NFA) (Radhakrishnan et al. 2024…
cs.LG2025
Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape
Ioannis Bantzis, James B. Simon, Arthur Jacot
When a deep ReLU network is initialized with small weights, gradient descent (GD) is at first dominated by the saddle at the origin in parameter space. We study the so-called escap…