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New theory offers closed-form depth-width law for neural networks

By PulseAugur Editorial · [1 sources] ·

Researchers have derived a closed-form depth-width law for neural collapse in deep linear networks, offering a way to understand how network architecture influences learned representations before training. This law, derived using neural collapse as a probe, expresses specific neural collapse metrics in terms of the learned feature kernel. The findings suggest that at large widths, the accumulated compression is a function of the depth-to-width ratio, with its rate determined by the data, prior, and architecture. AI

IMPACT Provides a theoretical framework for understanding neural network architecture and representation learning.

RANK_REASON Academic paper detailing theoretical findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New theory offers closed-form depth-width law for neural networks

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Academic paper detailing theoretical findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pankhil Gawade, Kirsten Fischer, Guy Wolf, Ewa Szczurek ·

    A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks

    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…

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